# Towards Intent-based User Interfaces: Charting the Design Space of Intent-AI
Interactions Across Task Types

Zijian Ding<EMAIL_ADDRESS>0000-0002-6372-0369 University of Maryland, College
ParkUSA

###### Abstract.

Technological advances continue to redefine the dynamics of human-machine
interactions, particularly in task execution. This proposal responds to the
advancements in Generative AI by outlining a research plan that probes intent-
AI interaction across a diverse set of tasks: fixed-scope content curation
task, atomic creative tasks, and complex and interdependent tasks. This
exploration aims to inform and contribute to the development of Intent-based
User Interface (IUI). The study is structured in three phases: examining
fixed-scope tasks through news headline generation, exploring atomic creative
tasks via analogy generation, and delving into complex tasks through
exploratory visual data analysis. Future work will focus on improving IUIs to
better provide suggestions to encourage experienced users to express broad and
exploratory intents, and detailed and structured guidance for novice users to
iterate on analysis intents for high quality outputs.

††conference: 2024 Google PhD Fellowship Research Proposal; ;

Figure 1. The spectrum of human intent expression ranges from one-off intent
expression for fixed-scope content curation tasks (Ding et al., 2023a), to
intent iteration with selecting, rating and post-editing for atomic creative
tasks (Ding et al., 2023b), and intent exploration, iteration and sensemaking
for complex and interdependent tasks (Ding and Chan, 2024). The spectrum is
based on human-AI interaction paradigms (Ding, 2024; Ding and Chan, 2023).

TODO

##  1\. Introduction

Human-computer interaction (HCI) involves humans expressing intent for a task,
which the machine then interprets, executes, and communicates the results back
to the user. Historically, the capabilities of machines to interpret human
intents and execute tasks were limited, resulting in complex and burdensome
processes for expressing these intents. This complexity often necessitated the
mastery of intricate programming languages for command-line interfaces or the
manipulation of buttons, dropdown menus, and other elements within graphical
user interfaces (GUIs). The advent of generative AI, particularly the
emergence of large language models, enables humans — at least for simple tasks
like text summarization — to convey their intents to machines using natural
language prompts, which are then translated into actions. Building on these
shifts in machine capabilities and interaction paradigms, we are inspired to
explore the concept of Intent-based User Interfaces (IUIs). Here, ”intent”
refers to the overarching purpose or objective at a higher level in the causal
chain of a task, rather than the specific steps such as direct manipulations
on complex GUIs. For instance, in a data analysis programming environment, the
primary intent is to understand trends in the data, as opposed to manually
inputting column names in plotting functions. This leads us to our research
question for the design space of IUI: What kinds of intent-AI interaction are
needed across different types of tasks?

To explore the design space of IUI, which bridges user intents with task
execution, we have investigated the types of intent-based interactions
required for various types of tasks.. Figure 1 illustrates a spectrum of these
intent-based interactions that extend from one-off expressions of intent for
fixed-scope content curation tasks like news headline generation (Ding et al.,
2023a), through intent iterations with selecting, rating and post-editing for
atomic creative tasks such as analogy-driven problem reformulation (Ding et
al., 2023b). Current work is exploring what intent-based interactions are
needed for complex and interdependent tasks,, such as exploratory visual data
analysis: here, we have some evidence that intent sensemaking with affordances
like exploration iteration, and curation are needed to support intent-based
interactions (Ding and Chan, 2024).

##  2\. Related work

My doctoral research studied the paradigms of intent expression on generative
AI across a spectrum of tasks, varying from simple to complex and
interdependent endeavors. This spectrum encompasses fixed-scope content
curation tasks, atomic creative tasks, and complex and interdependent tasks
(refer to Figure 1).

###  2.1. Fixed-scope Content Curation Tasks

Substantial research demonstrates that Large Language Models (LLMs) adeptly
manage defined content curation tasks such as text summarization (Goyal et
al., 2022), content refinement (Lin, 2023), and code explanation (Yan et al.,
2024). These tasks involve reiterating existing knowledge succinctly and
coherently without generating novel insights. LLMs have shown proficiency in
these tasks, often without the necessity for human intervention after
expressing their intents of tasks. For example, Clark et al. revealed that
text generated by LLM was linguistically advanced to the extent that
distinguishing it from human-written text became challenging for evaluators
(Clark et al., 2021).

###  2.2. Atomic Creative Tasks

The second category comprises atomic creative tasks that necessitate
generating novel and valuable outputs (Sawyer, 2012; Chan et al., 2024). These
include tasks such as crafting analogous design concepts (Zhu and Luo, 2022;
Lee et al., 2022), design problems (MacNeil et al., 2021b, a, c), slogans
(Clark et al., 2018), and tweetorials (Gero et al., 2022). Large Language
Models (LLMs) can foster novel connections or ”creative leaps” due to their
extensive knowledge base (Chan et al., 2017). However, real creativity often
requires domain-specific and nuanced knowledge that might be absent in LLM
training data. Hence, to ensure the final artifacts fulfill the task
requirement, LLM outputs for these tasks need to be iterated in the format of
guiding, selecting, or post-editing.

###  2.3. Complex and Interdependent Tasks

Beyond the atomic creative tasks are complex and interdependent tasks such as
active search (Palani et al., 2021b, a), data visualization (Vaithilingam et
al., 2024), video editing (Wang et al., 2024), active search (Palani et al.,
2021a, b), design workshops (MacNeil et al., 2023), neurocognitive disorders
screening (Ding et al., 2022), and storytelling in text (Yuan et al., 2022;
Singh et al., 2022) and images (Yan et al., 2023). These tasks demand not only
domain expertise but also capabilities for planning, reasoning, ideating, and
maintaining context over extended periods to create coherent and innovative
content. Researchers have developed LLM-supported tools like LAVE (Wang et
al., 2024) for video editing, DynaVis (Vaithilingam et al., 2024) for data
visualization, and XCreation (Yan et al., 2023) for cross-modal storytelling
to facilitate these intricate interactions.

##  3\. Study Plan and Preliminary Results

Our research comprises three studies, each examining intent-AI interactions
across tasks varying in creativity and complexity. The first study examines
fixed-scope content curation tasks, employing news headline generation as the
medium for exploration. The second study transitions to probing atomic
creative tasks, with a focus on analogy generation to uncover underlying
creative processes. Lastly, the third ongoing study explores the complex and
interdependent tasks associated with exploratory visual data analysis. We hope
to use these three studies to collectively offer a comprehensive understanding
of intent-AI interaction across a spectrum of task.

###  3.1. Study 1 on News Headline Generation: One-off Intent Expression for
Fixed-Scope Content Curation Tasks

Figure 2. Study 1 on fixed-scope content curation tasks - interface for human-
AI news headline co-creation for guidance + selection + post-editing
condition: (A) news reading panel, (B) perspectives (keywords) selection panel
(multiple keywords can be selected), (C) headline selection panel with post-
editing capability, and (D) difficulty rating slider.

This research explores intent-AI interactions within fixed-scope content
curation tasks, aiming to inform the design of Intent-based User Interfaces
(IUIs). We specifically examine the generation of news headlines from articles
as a representative task of text summarization (Ding et al., 2023a). We
conducted a between-subjects experiment involving the use of a large language
model (LLM), GPT-3 text-davinci-002, with 40 participants. These participants
were tasked with creating news headlines under six conditions:

  * •

Baseline: Utilization of headlines currently employed by online media.

  * •

One-off intent expression: Headlines generated by GPT-3.5 davinci-002 in
response to the prompt: ”Please generate three high-quality (attractive,
clear, accurate, and inoffensive) headlines for the following news article.”

  * •

Iteration with selection: The LLM generates three headlines, from which the
user selects the most fitting one.

  * •

Iteration with guidance and selection: The LLM identifies potential keywords
from each news article; the user selects one or more keywords to guide the
headline generation process, followed by the selection of the most fitting
headline.

  * •

Iteration with guidance, selection, and post-editing: Similar to the previous
condition, with an added feature allowing the user to edit the selected
headline. An example of this interactive interface is presented in Figure 2.

  * •

Manual only: Participants write the headlines independently, without AI
assistance.

The effectiveness of these conditions was assessed by 20 experts who evaluated
the generated headlines. The findings indicate that the one-off intent
expression condition was, on average, capable of producing high-quality
headlines without further iterations. This suggests that a simple textbox
interface such as chatbot can adequately serve as an IUI for one-off intent
expressions in fixed-scope content curation tasks similar to headline
generation.

###  3.2. Study 2 on Cross-Domain Analogy Generation: Intent Iteration for
Atomic Creative Tasks

Figure 3. Study 2 on atomic creative tasks - the prompt for the intent of
generating cross-domain analogies based on Duncker and Lees’ radiation problem
(Duncker, 1945).

Transitioning from fixed-scope content curation tasks, our second study
investigates atomic creative tasks, specifically focusing on cross-domain
analogy generation. Unlike one-off intent expressions for fixed-scope content
curation tasks, these tasks may require additional iterations to achieve high-
quality outputs due to their open-ended nature. In our exploratory
experiments, we evaluated the interaction between intent and AI in these tasks
using prompts that guide Large Language Models (LLMs) with classical problems
known for their analogical complexity, such as Duncker and Lees’ radiation
problem (Duncker, 1945) (illustrated in Figure 3).

To evaluate the effectiveness of cross-domain analogies generated by LLM
(GPT-3 text-davinci-002), participants were tasked with using these analogies
to reformulate original design problems (Ding et al., 2023b). The findings
were promising: the majority of AI-generated analogies were deemed useful,
receiving a median helpfulness rating of 4 out of 5. Furthermore, these
analogies facilitated significant changes in problem formulation in
approximately 80% of the cases. However, concerns were raised as up to 25% of
the outputs were considered potentially harmful due to the presence of
unsettling content. These insights highlight the potential of intent-AI
interaction in atomic creative tasks and simultaneously draw attention to the
necessity for intent iteration in the format of selection, rating, and post-
editing to address potential biases and ensure ethical and legal compliance.

###  3.3. Study 3 (in progress) on Exploratory Visual Data Analysis: Intent
Exploration, Iteration, and Sensemaking for Complex and Interdependent Tasks

Figure 4. Study 3 on exploratory visual data analysis - the participant
explored and iterated on analysis intents in natural language, and organized
generated visualization on a large display.

We extended our investigation into intent-AI interactions to complex and
interdependent tasks, specifically focusing on exploratory visual data
analysis. As a preliminary study, we developed a freeform canvas as Intent-
based User Interface (IUI) where users can swiftly articulate their analytical
intents using natural language prompts to generate and iteratively refine
visualizations as ”space to think” (Knudsen et al., 2012). This process also
includes the (re)organization of visualization results for sensemaking on a
large display (illustrated in Figure 4).

To understand how users engage with this experimental interface, we conducted
a user study involving 10 data analysts, ranging from novices to experienced
professionals, complemented by semi-structured interviews. Initial findings
revealed differences in interaction patterns between the two groups.
Experienced data analysts predominantly engaged in iterative refinement of
their visualizations, often using detailed, command-like expressions of
intent, such as ”Correlation plot of data - (+) correlation coefficient in
red, (-) correlation coefficient in blue; include all variables that are
continuous”. This approach yielded high-quality outputs but limited the
exploration of alternative analytical perspectives, potentially overlooking
the exploratory nature of the task.

Conversely, novice analysts were more inclined to explore broadly, utilizing
higher-level intent expressions like ”What is the relationship between density
and birth rate?” or ”Can you tell me the trend?” However, the outputs
generated from these prompts were of lower quality, indicating that novices
might benefit from more structured guidance and scaffolding throughout their
analytical endeavors.

These observations highlight the diverse needs of users at different levels of
expertise when interacting with intent-driven AI tools in complex and
interdependent tasks. They emphasize the necessity for IUI that can better
support both broad exploratory intent expressions and detailed intent
iterations. In the following studies, I will further explore the development
of IUI for such tasks, focusing on providing experienced users with
suggestions to engage more with high-level intents rather than solely focusing
on iterations, and more structured guidance and scaffolds in IUI to aid novice
users in achieving higher quality results.

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evaluated with the POTQ circuit as a subroutine within a classical gradient-
descent algorithm. The overall query complexity in the number of calls to the
cost evaluation routine of Algorithm 3 is $O(NTL/\delta^{2})$, where
$\delta=1/\sqrt{n_{\text{shots}}}$ is the sample precision, $N$ is the maximum
number of repetitions over random initial parameters
$\boldsymbol{\alpha}^{0}$, $L$ is the dimension of the continuous parameter
space of $\boldsymbol{\alpha}$, and $T$ is the number of gradient descent
iterations for a suitable learning rate $\eta>0$. In order to improve
convergence, it may also be useful to supply the quantum subroutines for
computing the cost function and the gradient to a more advanced minimization
routine, for example as found in the Python library SciPy [62]. We present
below the results on compiling both single-qubit and two-qubit gates on a
simulator.

When performing Algorithm 3, we rely on the ability to perform the
controlled-$U$ gate. The unitary $U$ may be unknown, e.g., as in Fig. 1(b). In
general, to perform a controlled operation with respect to a target unitary
$U$, one can use a method for “remote control” [63]. This method employs a
local $U$ gate and controlled-SWAP operations in order to realize the
controlled-$U$ gate. In practice, since any controlled unitary gate can be
decomposed into native gates, the ability to compile controlled-SWAP, the
Toffoli gate, and the set of controlled rotations is sufficient. In order to
perform such a translation, we allow the user to have access to a small-scale
classical compiler. This does not incur exponential overhead since the gates
to be translated are one- and two-qubit gates (or their controlled versions).
While this may cause the depth of our compiled unitary to increase, it will
only be by a constant factor.

We note that decoherence, gate infidelity, and readout errors on NISQ
computers are all more pronounced when attempting to execute controlled
unitaries. This means that there is significant performance loss for
controlled unitaries, as required in the POTQ. Consequently, we did not
implement our gradient-based optimization method on current quantum devices,
but we speculate that improvements to quantum hardware will enable this
application.

####  F.2.1 Implementation on a quantum simulator

Figure 14:  Compiling one- and two-qubit gates on a simulator with the gate
alphabet in (35) using the gradient-based optimization technique described in
Algorithm 3, with $n_{\text{shots}}=10,000$. Shown is the cost as a function
of the number of gradient calls of the continuous parameter optimization using
the minimize routine in the SciPy-optimize Python library. The gate structure
for the single-qubit gates is fixed to the one shown in Fig. 12(a), while the
gate structure for the two-qubit gates is fixed to the one shown in Fig.
12(b).

We use IBM’s simulator [30] to compile a selection of single-qubit and two-
qubit gates by performing the gradient-based optimization procedure in
Algorithm 3. In order to improve convergence, we additionally supply the
gradient, as well as the cost function, to the minimize routine in the SciPy-
optimize Python library [62]. For the single-qubit gates, we assume a fixed
structure for the trainable gate sequence according to the decomposition in
Fig. 12(a), while for the two-qubit gates we assume a fixed structure for the
trainable gate sequence according to the decomposition in Fig. 12(b). We
compile the $T$ gate, $X$ gate, Hadamard ($H$) gate, as well as the CNOT and
CZ gates, all with $n_{\text{shots}}=10,000$. The results are shown in Fig.
14. We note that increasing $n_{\text{shots}}$ to higher orders of magnitude
significantly reduces the sampling error and results in more stable
convergence at the cost of an increase in runtime.

###  F.3 Gradient-based optimization via the HST and LHST

We now show that it is possible to perform gradient-based optimization of the
original cost function $C_{\text{HST}}$ and its local variant
$C_{\text{LHST}}$. This allows us to perform gradient-based optimization of
the general cost function $C_{q}=qC_{\text{HST}}+(1-q)C_{\text{LHST}}$. The
algorithm for gradient-based optimization of $C_{\text{HST}}$ and
$C_{\text{LHST}}$ is presented in Algorithm 4.

The gradient with respect to $\boldsymbol{\alpha}$ of both
$C_{\text{HST}}(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}))$ and
$C_{\text{LHST}}(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}))$ can be computed
using the HST and the LHST, respectively. Specifically, for a gate sequence of
the form in (77), in which the only parameterized gates are the single-qubit
rotation gates, we have that

$\displaystyle\frac{\partial}{\partial\alpha_{\ell}}C_{\text{HST}}(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}))$
$\displaystyle=\frac{1}{2}C_{\text{HST}}(U,\widehat{V}_{\boldsymbol{k},+}^{(\ell)}(\boldsymbol{\alpha}))$
(87)
$\displaystyle\quad-\frac{1}{2}C_{\text{HST}}(U,\widehat{V}_{\boldsymbol{k},-}^{(\ell)}(\boldsymbol{\alpha})),$

and

$\displaystyle\frac{\partial}{\partial\alpha_{\ell}}C_{\text{LHST}}^{(j)}(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}))$
$\displaystyle=\frac{1}{2}C_{\text{LHST}}^{(j)}(U,\widehat{V}_{\boldsymbol{k},+}^{(\ell)}(\boldsymbol{\alpha}))$
(88)
$\displaystyle\quad-\frac{1}{2}C_{\text{LHST}}^{(j)}(U,\widehat{V}_{\boldsymbol{k},-}^{(\ell)}(\boldsymbol{\alpha}))$

for all $j\in\\{1,2,\dotsc,n\\}$. Here,

$\displaystyle\widehat{V}_{\boldsymbol{k},\pm}^{(\ell)}(\boldsymbol{\alpha})$
$\displaystyle\coloneqq G_{k_{L}}(\alpha_{L})\dotsb
G_{k_{\ell+1}}(\alpha_{\ell+1})G_{k_{\ell}}\left(\pm\frac{\pi}{2}\right)$ (89)
$\displaystyle\quad\quad\times
G_{k_{\ell}}(\alpha_{\ell})G_{k_{\ell-1}}(\alpha_{\ell-1})\dotsb
G_{k_{1}}(\alpha_{1})$

is the original gate sequence $V_{\boldsymbol{k}}(\boldsymbol{\alpha})$ with
an additional rotation gate $G_{k_{\ell}}\left(\pm\frac{\pi}{2}\right)$
corresponding to the variable with respect to which the derivative is taken.
In other words, to compute the gradient of the cost function
$C_{\text{HST}}(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}))$, we run the HST in
Fig. 4(a) twice, once with the gate sequence
$\widehat{V}_{\boldsymbol{k},+}^{(\ell)}(\boldsymbol{\alpha})$ and once with
the gate sequence
$\widehat{V}_{\boldsymbol{k},-}^{(\ell)}(\boldsymbol{\alpha})$. Similarly, to
compute the gradient of the functions
$C_{\text{LHST}}^{(j)}(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}))$, we run the
LHST in Fig. 4(b) twice, once with the gate sequence
$\widehat{V}_{\boldsymbol{k},+}^{(\ell)}(\boldsymbol{\alpha})$ and once with
the gate sequence
$\widehat{V}_{\boldsymbol{k},-}^{(\ell)}(\boldsymbol{\alpha})$.

The expressions for the gradient in (87) and (88) can be verified by recalling
that only the one-qubit gates need to be parameterized and that they can
always be assumed to have the form $e^{-i\alpha\sigma/2}$ for some Pauli
operator $\sigma$, where $\alpha$ is the continuous parameter specifying the
gate. Then, for the gate sequence $V_{\boldsymbol{k}}(\boldsymbol{\alpha})$ in
(77), we get

$\displaystyle\frac{\partial
V_{\boldsymbol{k}}(\boldsymbol{\alpha})}{\partial\alpha_{\ell}}$
$\displaystyle=G_{k_{L}}(\alpha_{L})\dotsb
G_{k_{\ell+1}}(\alpha_{\ell+1})\frac{\partial
G_{k_{\ell}}(\alpha_{\ell})}{\partial\alpha_{\ell}}$
$\displaystyle\qquad\times G_{k_{\ell-1}}(\alpha_{\ell-1})\dotsb
G_{k_{1}}(\alpha_{1})$ (90)
$\displaystyle=-\frac{i}{2}G_{k_{\ell}}(\alpha_{\ell})\dotsb
G_{k_{\ell+1}}(\alpha_{\ell+1})\sigma_{k_{\ell}}G_{k_{\ell}}(\alpha_{\ell})$
$\displaystyle\qquad\times G_{k_{\ell-1}}(\alpha_{\ell-1})\dotsb
G_{k_{1}}(\alpha_{1})$ (91)

Then, we use the identity

$\displaystyle i[\sigma_{k_{\ell}},\rho]$
$\displaystyle=G_{k_{\ell}}\left(-\frac{\pi}{2}\right)\rho
G_{k_{\ell}}\left(-\frac{\pi}{2}\right)^{\dagger}$ (92)
$\displaystyle\qquad\qquad\qquad-G_{k_{\ell}}\left(\frac{\pi}{2}\right)\rho
G_{k_{\ell}}\left(\frac{\pi}{2}\right)^{\dagger},$

which holds for any state $\rho$. We also observe that both the functions
$C_{\text{HST}}(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}))$ and
$C_{\text{LHST}}^{(j)}(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}))$ are of the
form

$\displaystyle F(\boldsymbol{\alpha})={\rm Tr}[H(U\otimes
V_{\boldsymbol{k}}(\boldsymbol{\alpha})^{*})\rho(U^{\dagger}\otimes
V_{\boldsymbol{k}}(\boldsymbol{\alpha})^{T})],$ (93)

where
$\rho={\left|{\Phi^{+}}\right\rangle}{\left\langle{\Phi^{+}}\right|}_{A_{1}\dotsb
A_{n}}$ for both functions,
$H={\left|{\Phi^{+}}\right\rangle}{\left\langle{\Phi^{+}}\right|}_{A_{1}\dotsb
A_{n}}$ for $C_{\text{HST}}(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}))$, and
$H={\left|{\Phi^{+}}\right\rangle}{\left\langle{\Phi^{+}}\right|}_{A_{j}B_{j}}\otimes\mathbbm{1}_{\bar{A_{j}}\bar{B_{j}}}$
for $C_{\text{LHST}}^{(j)}(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}))$.
Finally, using

$\displaystyle\frac{\partial
F(\boldsymbol{\alpha})}{\partial\alpha_{\ell}}={\rm
Tr}\left[H\left(U\otimes\left(\frac{V_{\boldsymbol{k}}(\boldsymbol{\alpha})}{\partial\alpha_{\ell}}\right)^{*}\right)\rho(U^{\dagger}\otimes
V_{\boldsymbol{k}}(\boldsymbol{\alpha})^{T})\right]$ $\displaystyle+{\rm
Tr}\left[H(U\otimes
V_{\boldsymbol{k}}(\boldsymbol{\alpha})^{*})\rho\left(U^{\dagger}\otimes\left(\frac{\partial
V_{\boldsymbol{k}}(\boldsymbol{\alpha})}{\partial\alpha_{\ell}}\right)^{T}\right)\right],$
(94)

substituting (91) into this expression, and using (92) to simplify, we obtain
(87) and (88).

Input: Unitary $U$ to be compiled; a trainable unitary
$V_{\boldsymbol{k}}(\boldsymbol{\alpha})$ of a given structure, where
$\boldsymbol{\alpha}$ is a continuous circuit parameter of dimension $L$;
maximum number of iterations $N$; gradient tolerance
$\varepsilon^{\prime}\in(0,1)$; sample precision $\delta>0$; cost function
$C\in\\{C_{\text{HST}},C_{\text{LHST}}\\}$.

Output: Parameters $\boldsymbol{\alpha}_{\text{opt}}$ such that at best
$|\\!|\nabla_{\boldsymbol{\alpha}}C(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha_{\text{opt}}}))|\\!|^{2}\leqslant\varepsilon^{\prime}$.

Init: $\boldsymbol{\alpha}_{\text{opt}}\leftarrow 0$; $\texttt{cost}\leftarrow
0$; $\texttt{grad}\leftarrow\infty$; $\tau~{}\leftarrow~{}0$;
$\texttt{gradCount}\leftarrow 0$; $\eta\leftarrow 1$

1 choose initial parameters $\boldsymbol{\alpha}^{(0)}$ at random

2 $\texttt{cost}\leftarrow C(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}^{(0)}))$

3

4while  _${\texttt{count}} <N$ and ${\texttt{gradCount}}<4$_ do

5 $\tau\leftarrow\tau+1$

6 for  _$i=1,2,\dotsc,L$_ do

7 Calculate $\frac{\partial C}{\partial\alpha_{i}}$ using either (87) or (88),
taking approximately $\frac{1}{\delta^{2}}$ samples for each circuit.

8

9
$\texttt{grad}\leftarrow|\\!|\nabla_{\boldsymbol{\alpha}}C(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}^{(\tau-1)}))|\\!|^{2}$

10

11 if  _${\texttt{grad}}\leqslant\varepsilon^{\prime}$_ then

12 $\texttt{gradCount}\leftarrow\texttt{gradCount}+1$

13

14
$\boldsymbol{\alpha}_{1}^{(\tau-1)}\leftarrow\boldsymbol{\alpha}^{(\tau-1)}-\eta\nabla_{\boldsymbol{\alpha}}C(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}^{(\tau-1)}))$

15

16
$\boldsymbol{\alpha}_{2}^{(\tau-1)}\leftarrow\boldsymbol{\alpha}_{1}^{(\tau-1)}-\eta\nabla_{\boldsymbol{\alpha}}C(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}^{(\tau-1)}))$

17

18 if
_${\texttt{cost}}-C(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}_{2}^{(\tau-1)}))\geqslant\eta\cdot{\texttt{grad}}$_
then

19 $\eta\leftarrow 2\eta$

20 $\alpha^{(\tau)}\leftarrow\alpha_{2}^{(\tau-1)}$

21

22 else if
_${\texttt{cost}}-C(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}_{1}^{(\tau-1)}))
<\frac{\eta}{2}\cdot{\texttt{grad}}$_ then

23 $\eta\leftarrow\frac{\eta}{2}$

24 $\alpha^{(\tau)}\leftarrow\alpha_{1}^{(\tau-1)}$

25 else

26 $\alpha^{(\tau)}\leftarrow\alpha_{1}^{(\tau-1)}$

27

28 $\texttt{cost}\leftarrow
C(U,V_{\boldsymbol{k}}(\boldsymbol{\alpha}^{(\tau)}))$

29

30 $\boldsymbol{\alpha}_{\text{opt}}\leftarrow\boldsymbol{\alpha}^{(\tau)}$

31

32return _$\boldsymbol{\alpha}_{\textnormal{opt}},{\texttt{cost}}$_

Algorithm 4 Gradient-based Continuous
Optimization for QAQC via the HST and LHST


# Causal Intervention Improves Implicit Sentiment Analysis

Siyin Wang1, Jie Zhou1 Jie Zhou is the corresponding authors of this paper.
Changzhi Sun2 Junjie Ye1
Tao Gui1 Qi Zhang1 Xuanjing Huang1
1School of Computer Science, Fudan Univerisity, Shanghai, China 2ByteDance AI
Lab
{siyinwang20, jie_zhou, jjye19, tgui, qz<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Despite having achieved great success for sentiment analysis, existing neural
models struggle with implicit sentiment analysis. This may be due to the fact
that they may latch onto spurious correlations (“shortcuts”, e.g., focusing
only on explicit sentiment words), resulting in undermining the effectiveness
and robustness of the learned model. In this work, we propose a causal
intervention model for Implicit Sentiment Analysis using Instrumental Variable
(ISAIV). We first review sentiment analysis from a causal perspective and
analyze the confounders existing in this task. Then, we introduce an
instrumental variable to eliminate the confounding causal effects, thus
extracting the pure causal effect between sentence and sentiment. We compare
the proposed ISAIV model with several strong baselines on both the general
implicit sentiment analysis and aspect-based implicit sentiment analysis
tasks. The results indicate the great advantages of our model and the efficacy
of implicit sentiment reasoning.

##  1 Introduction

The remarkable success that the field of sentiment analysis has achieved in
the past few years has been derived from the use of increasingly high-capacity
neural models to extract correlations from data Peters et al. (2018); Devlin
et al. (2018); Liu et al. (2019). Although having reached state-of-the-art
results, correlational predictive models can be untrustworthy Guidotti et al.
(2018): they may latch onto spurious correlations (“shortcuts”), leading to
poor generalization.

One shortcut might be the explicit sentiment word which is a powerful feature
cue. Unfortunately, such a shortcut severely harms the generalization and the
robustness of the learned models in implicit sentiment analysis (ISA), where
there are no explicit sentiment words about the topic or aspect Russo et al.
(2015). Figure 1 gives a sample of aspect-based sentiment analysis (ABSA) Zhou
et al. (2020b, a), which aims to predict the sentiments of the aspects in the
sentence. The aspect “food” has explicit sentiment words “definitely good”,
but aspect “price” does not. If the model thoughtlessly relies on shortcuts to
sentiment words, it may make an incorrect sentiment prediction about the
aspect “price”. In fact, there are many other kinds of shortcuts that models
may learn, for example, the rhetorical question mood expressed by the users
Ranganath et al. (2018) and the co-occurrence of neutral words and sentiment
polarities Wang and Culotta (2020).

The above shortcomings can potentially be addressed by the causal perspective:
knowledge of causal relationships between observations and labels can be used
to formalize spurious correlations and alleviate the predictor’s dependence on
them Bühlmann (2020); Veitch et al. (2021); Feder et al. (2021). Motivated by
a causal perspective, we incorporate domain knowledge of the causal structure
of the data into the learning objective. Specifically, causal intervention is
used to curb dependence on shortcuts (e.g., “good $\rightarrow$ positive”) and
improve the ability to reason causal effect between sentence and sentiment.

Figure 1: An examples of confounding factors in implicit sentiment analysis
for ABSA.

In this paper, we rethink the ISA task from a causal perspective and unitize
the casual intervention on deep learning. We argue that the causal effect
obtained by reasoning directly from the sentence ($X$) to the sentiment ($Y$)
without relying on other extra prior stereotypical lexical impressions is
closer to the original semantic analysis. Our work aims at eliminating the
confounding causal effects of $C\rightarrow Y$ and thus extracting the pure
causal effect between sentence and sentiment. Inspired by the instrumental
variable in causality, we propose a causal intervention model for ISA using
instrumental variable (ISAIV). Different from the other work with causal
intervention like back-door adjustment Landeiro and Culotta (2016), other
variables like confounders are not required to be observed. ISAIV contains
two-stage learning: (1) In the first stage, we model the relationship between
the instrumental variable and sentence; (2) In the second stage, we dismiss
the spurious correlation between confounders and sentiment by means of the
relationship obtained from the first stage.

To evaluate the effectiveness of our ISAIV, we conduct a series of experiments
on both the general implicit sentiment analysis and aspect-based implicit
sentiment analysis. In particular, we compare our model with several the
mainstream baselines and the results show the great advantages of our model on
ISA. We also validate the robustness of the model by adversarial attack and
case studies, which proves that our model successfully dismisses the spurious
correlation caused by sentiment words and extracts the pure causal effect.

The main contributions are summarized as follows.

  * •

We rethink the implicit sentiment from a causal perspective and proposed a
casual intervention model for implicit sentiment analysis (ISAIV).

  * •

To remove the spurious causes of confounders, we incorporate instrumental
variable into neural network to enhance its causal reasoning ability.

  * •

We conduct experiments on diverse datasets, including partially implicit and
totally implicit sentiment, which shows our effectiveness and rationality to
reason implicit sentiment.

##  2 Preliminaries

###  2.1 Structural Causal Model and Causal Effect

In our paper, Structural Causal Model (SCM) Glymour et al. (2016) is
represented as a directed acyclic graphs (DAGs) $G=\left\\{V,E\right\\}$ to
reflect causal relationships, where $V$ denotes the set of observational
variables and $E$ denotes the direct causal effect (Figure 2(a)). $X$ is a
direct cause of $Y$ when variable $Y$ is the child of $X$.

Variable $X$ and $Y$ is called treatment variable and outcome variable
respectively when observing the causal relationship between them. The other
variables we do not consider their causal relationship are called error terms
($\varepsilon$), also known as exogenous variables. Significantly, total
effect between $X$ and $Y$, denoted as $P(Y\mid X)$, is conceptually different
from causal effect of $X\rightarrow Y$, denoted as $P(Y\mid do(X=x))$ because
the causal effect only involves the direct path from $X$ to $Y$, while the
total effect involves all paths between $X$ and $Y$. Based on the ideal
hypothesis that none of the error terms will involve in the path between $X$
and $Y$, people usually treat the total effect and the causal effect as the
same. But the actual fact is that a part of error terms (we call it confounder
($C$)) serves as a common cause of the treatment and outcome, denoted as
$X\leftarrow C\rightarrow Y$. Consequently, the total effect is practically
different from the causal effect, i.e. $P(Y\mid do(X=x))\neq P(Y\mid X)$ and
treatment-outcome relationship may well be obscured by the spurious
correlation between $C$ and $Y$ generated by confounder Pearl (2009); Hernán
MA (2020).

(a) Causal Graph

(b) Instrument Variable for Causal Intervention

Figure 2: Causal Graph

###  2.2 Instrument Variable for Causal Intervention

To recover the gap between total effect $P(Y\mid X)$ and casual effect
$P(Y\mid do(X=x))$ and derive pure causal effect, we must “adjust” for
potential confounder ($C$) Pearl (1995). Fortunately, applying causal
intervention can extract the pure causality from the correlation and therefore
overcome the problem of confounding bias. There are four key interventions:
randomized controlled trial, backdoor adjustment, front-door adjustment, and
instrumental variable estimation. Randomized controlled trials are simply not
practicable for natural language, and both the front-door and back-door
adjustment require additional observable variables. However, the confounders
(e.g., rhetoric confounding word, such as rhetorical questions and sarcasm)
are too polymorphic to be observed exhaustively for implicit sentiment
analysis. We adopt the instrumental variable to dismiss the spurious
correlations instead of directly observing confounders (Figure 2(b)).

Before the intervention, we should find a suitable instrumental variable ($Z$)
that qualifies well the requirements as follows:Brito and Pearl (2012)

1\. $Z$ is independent of all error terms $\varepsilon$ that have an influence
on $Y$ which is not mediated by $X$, $Cov(Z,\varepsilon)=0$.

2\. $Z$ is not independent of $X$, $Cov(Z,X)\neq 0$.

The intuition behind this definition is that all correlation between $Z$ and
$Y$ requires $X$ to act as an intermediary.

Generally, instrument variable estimation contains two stages Angrist and
Pischke (2008). In the first stage, the coefficient $\alpha$ is obtained by
regression estimation of $X$ and $Z$, denoted as ${Cov(Z,X)}$. In the second
stage, substitute $X$ with the expressions including $Z$ obtained in the first
stage into the expression of $Y$ and then regress $Y$ on $Z$ , denoted as
${Cov(Z,Y)}$. There is no confounding bias between $Y$ and $Z$ owing to the
restriction in the definition of $Z$, i.e. ${Cov(Z,\varepsilon)}=0$ . A simple
linear model for IV estimation consists of 2 equations:

$\displaystyle X=\alpha Z+\varepsilon_{X};Y=\omega X+\varepsilon_{Y}$ (1)

where $Y$ is the outcome variable (e.g., sentiment), $X$ is the treatment
variable (e.g, sentence), $Z$ is the instrumental variable (e.g., stochastic
perturbation), and $\varepsilon_{X}$ and $\varepsilon_{Y}$ are error terms
including but not limited to confounders(C). Under the conditions above, it
can be proved that the equation presents an asymptotically unbiased estimate
of the effect of $X$ on $Y$ Angrist et al. (1996).

${\omega_{IV}}=\frac{\frac{1}{n}\sum_{i=1}^{n}(z_{i}-\overline{z})(y_{i}-\overline{y})}{\frac{1}{n}\sum_{i=1}^{n}(z_{i}-\overline{z})(x_{i}-\overline{x})}=\frac{Cov(Z,Y)}{Cov(Z,X)}$
(2)

Figure 3: The framework of our ISAIV.

##  3 Our Approach

In this section, we introduce our ISAIV model for implicit sentiment analysis
(Figure 3). We first rethink the ISA from a causal perspective (Section 3.1).
Then, we adopt stochastic perturbation as instrumental variable (Section 3.2)
and estimate instrumental variable in two stages (Section 3.3 and 3.4).

###  3.1 Sentiment Analysis from Casual Perspective

Given a sentence $X$, consisting of a sequence of tokens
$(x_{1},x_{2},...,x_{n})$, our task aims to analyze the polarity $Y$. For
aspect-based sentiment analysis task, we concatenate the sentence and the
aspect as the input $X=(x_{1},x_{2},...,x_{n},[SEP],a_{1},a_{2},...,a_{m})$.
In the current method, a deep neural network is used as a classifier to
predict the sentiment polarity label as output and the sentence as input (as
Equation 3).

$y=h(x;w)=W_{xy}\cdot x+\varepsilon_{y}$ (3)

where $\varepsilon_{y}$ denotes as the error terms including confounders ($c$)
and other errors ($\hat{\varepsilon_{y}}$).

The prediction above is based on the hypothesis that error terms will not
involve in the causal path between $X$ and $Y$ and ignore the influence of
error terms mostly. Nevertheless, several research has found that text
classification systems based on neural networks are biased towards learning
frequent spurious correlations Leino et al. (2018). It urges us to focus on
the longtime unheeded but unavoidable existence of confounder ($C$) in error
terms, which results in the overlooked gap between total effect and causal
effect, denoted as the path $X\leftarrow C\rightarrow Y$. The Equation 3 can
be updated in consideration of confounder (c) (Equation 4).

$y={h}(x;w)\\\ =W_{xy}\cdot x+W_{cy}\cdot c+\hat{\varepsilon_{y}}$ (4)

where $c$ and $\hat{\varepsilon_{y}}$ denotes the confounder and other error
terms respectively, $W_{cy}$ denotes the causal effect of $C\rightarrow Y$.

In previous studies, gender Field and Tsvetkov (2020), age, and address
Landeiro and Culotta (2016) were found to be confounders in text
classification. As for ISA, we focus rather on the naturally existing
confounder within the text, i.e., sentiment words. Sentiment words affect the
form of the text as a component of the text (i.e., the writer’s word choice
determines the form of expression) and also affect sentiment expression Xing
et al. (2020). The existence of sentiment words as confounder makes it
difficult to distinguish the pure causal effect of $X\rightarrow Y$ and the
prediction indiscriminately depends on the spurious correlation between
sentiment words and sentiment will fail in ISA. The main forms are as follows:

$\bullet$ Inter-aspect Confounding Word Explicit sentiment words of other
aspects with opposite sentiment in the sentence confounds the prediction
effect of the current aspect.

$\bullet$ Inter-clause Confounding Word In an adversative compound sentence,
the other clauses with opposite semantics confound the prediction effect of
the current clause.

$\bullet$ Rhetoric Confounding Word Sentiment words conveying the opposite of
the norm in rhetorical devices such as rhetorical questions and sarcasm
confound the prediction effect.

$\bullet$ Dynamic Neural Confounding Word Neutral words show dynamic sentiment
polarity in different contexts, but the model trained by biased data only
learns the spurious correlation of one polarity.

We also provide a detailed analysis of the confounder in case studies (Section
5.2).

Inspired by causal intervention with instrumental variable (Section 2.2), we
adopt two-stage instrument variable estimation for ISA to achieve the goal
that distinguishes the pure causal effect of $X\rightarrow Y$ without any
spurious correlations, denoted as $P(Y\mid do(X=x))$.

Table 1: The statistics information of the datasets. IS means the percent of samples that are implicit sentiment. 4 Dataset | Postive | Neural | Negative | IS (%)
---|---|---|---|---
Train | Test | Train | Test | Train | Test | Train | Test
Restaurant | 2164 | 728 | 805 | 196 | 633 | 196 | 28.59 | 23.84
Laptop | 987 | 341 | 866 | 128 | 460 | 169 | 30.87 | 27.27
CLIPEval | 435 | 144 | 205 | 72 | 640 | 155 | 100.00 | 100.00
4 |  |  |  |  |  |  |  |

###  3.2 Stochastic Perturbation as Instrumental Variable

For text, the two restrictions of instrumental variable could be translated
into two basic opinions: (1) instrumental variable $Z$ will not influence the
sentiment polarity via any other casual path except through sentence $X$; (2)
instrumental variable $Z$ will influence the format of sentence $X$.
Intuitively, we choose the stochastic perturbation as the instrumental
variable of ISA. Inspired by the work of data augmentation Guohang et al.
(2020), we choose random swap, random deletion, random insertion, and synonym
substitution as stochastic perturbation: A) Random Swap: Swap word randomly;
B) Random Deletion: Delete word randomly with probability $p$; C) Random
insertion: Insert word randomly by word embeddings similarity; D) Synonym
Substitution: Substitute word by WordNet’s synonym. It fortunately meets the
requirements of instrumental variable well: (1) stochastic perturbation
obviously has no independent effect on sentiment, except through augmentation
sentences, i.e. $Cov(Z,\varepsilon)=0$; (2) stochastic perturbation above will
definitely change the sentence into another form, i.e. $Cov(Z,X)\neq 0$.

###  3.3 The First Stage of ISAIV

Following the traditional pattern of instrumental variable estimation (Section
2.2), the first stage of ISAIV is to establish the causal relationship between
stochastic perturbation ($Z$) and sentence ($X$), i.e. $Z\rightarrow X$. We
use two open source tools111https://github.com/jasonwei20/eda_nlp
https://github.com/makcedward/nlpaug to generate augmentation samples $x_{z}$
from the original sample $x$ and the formal expression is as follows.

$x_{z}=f(x,z)$

where $f(\cdot)$ denotes the different stochastic perturbation on the original
sentence. For a specific stochastic perturbation $z_{i}$, we have
$x_{z_{i}}=f(x,z_{i})\approx\alpha_{i}\cdot x$. To obtain the accurate value
of $\alpha$ which can well represent the relationship between $x$ and $x_{z}$,
a neural network was constructed based on the BERT and $\alpha$ was set as a
self-learning parameter.

$\displaystyle\alpha_{i}=\mathop{\min}\limits_{\alpha}\sum_{x_{z_{i}}=f(x,z_{i})}\parallel\mathcal{M}(x_{z_{i}})-\alpha\cdot\mathcal{M}(x)\parallel$

where $\mathcal{M}$ denotes a text encoder (e.g., BERT).

###  3.4 The Second Stage of ISAIV

Substituting the relation above between original sample $x$ and augmentation
sample $x_{z_{i}}$ into Equation 4, we will get the $y\mid_{x=x_{z_{i}}}$ with
different proportionality coefficient $\alpha$ obtained from the first stage.

$\displaystyle y\mid_{x=x_{z_{i}}}$ $\displaystyle=W_{xy}\cdot
x_{z_{i}}+W_{cy}\cdot c+\hat{\varepsilon_{y}}$ (5)
$\displaystyle=\alpha_{i}\cdot W_{xy}\cdot x+W_{cy}\cdot
c+\hat{\varepsilon_{y}}$ $\displaystyle=\alpha_{i}\cdot
y\mid_{do(x=x)}+W_{cy}\cdot c+\hat{\varepsilon_{y}}$

where $y\mid_{do(x=x)}$ denotes the prediction only along the path
$X\rightarrow Y$.

As the neural network is not totally linear, we slightly generalize the usage
of two stages in instrumental variable. We set the dismission of influence of
the confounder as a regularization function instead of directly calculating
the effect between $X$ and $Y$ as a single value (Equation 2), which is
obviously more fit for deep learning method.

$\displaystyle\mathcal{L}_{IV}$ $\displaystyle=\sum_{i\neq
j}\parallel\alpha_{j}\cdot y\mid_{x=x_{z_{i}}}-\alpha_{i}\cdot
y\mid_{x=x_{z_{j}}}\parallel$

The reason we just model the prediction $y\mid_{x=x_{z_{i}}}$ and unitize the
regularization loss on it is that the essence of the $\mathcal{L}_{IV}$ is to
force the model to suppress the confounding effect caused by sentiment words.
It can be easily proved by substituting the $y\mid_{x=x_{z_{i}}}$ with
Equation 5 obtained by two-stage learning. The benefit is obviously that we
can suppress the confounding effect without directly observing the confounders
(c).

$\mathcal{L}_{IV}=\sum_{i\neq
j}\parallel(\alpha_{i}-\alpha_{j})\cdot(W_{cy}\cdot
c+\hat{\varepsilon_{y}})\parallel$

In addition, the model should not go to the other extreme, i.e., ignore
sentiment words entirely, which would be inconsistent with the normal process
of natural language understanding. We set a hyperparameter $\beta$ to achieve
balance and combine the causal regularization loss function $\mathcal{L}_{IV}$
with the conventional cross-entropy loss $\mathcal{L}_{CE}$ and the influence
of the $\beta$ is analyzed in Section 5.3.

$\mathcal{L}_{ALL}=\mathcal{L}_{CE}+\beta\mathcal{L}_{IV}$

##  4 Experiment

Table 2: The main results for aspect-based sentiment analysis. For ESE and
ISE, we provide the F1 score. We use the results reported in Li et al.
(2021b). The baselines with † are our implementation.

4 | Restaurant |  Laptop
---|---|---
| Acc | F1 | ESE | ISE | Acc | F1 | ESE | ISE
| ATAE-LSTM | 76.90 | 62.64 | 84.16 | 53.71 | 65.37 | 62.92 | 75.69 | 37.86
| IAN | 76.88 | 67.71 | 86.52 | 46.07 | 67.24 | 63.72 | 75.86 | 44.25
| RAM | 80.23 | 70.80 | 85.11 | 55.81 | 74.49 | 71.35 | 75.86 | 44.25
| MGAN | 81.25 | 71.94 | 85.18 | 60.04 | 75.39 | 72.47 | 76.16 | 56.31
NN | TransCap | 79.55 | 71.41 | 86.52 | 59.93 | 73.87 | 70.10 | 77.16 | 60.34
| ASGCN | 80.77 | 72.02 | 84.29 | 62.91 | 75.55 | 71.05 | 75.46 | 57.77
| BiGCN | 81.97 | 73.48 | 87.19 | 59.05 | 74.59 | 71.84 | 79.53 | 62.64
| CDT | 82.30 | 74.02 | 88.79 | 65.87 | 77.19 | 72.99 | 77.53 | 68.90
GNN | RGAT | 83.30 | 76.08 | 89.45 | 61.05 | 77.42 | 73.76 | 80.17 | 65.52
| BERT-SPC | 83.57 | 77.16 | 89.21 | 65.54 | 78.22 | 73.45 | 81.47 | 69.54
| CapsNet+BERT | 85.09 | 77.75 | 91.68 | 64.04 | 78.21 | 73.34 | 82.33 | 67.24
| BERT-PT | 84.95 | 76.96 | 92.15 | 64.79 | 78.07 | 75.08 | 81.47 | 71.27
| BERT-ADA | 87.14 | 80.05 | 94.14 | 65.92 | 78.96 | 74.18 | 82.76 | 70.11
| R-GAT+BERT | 86.60 | 81.35 | 92.73 | 67.79 | 78.21 | 74.07 | 82.44 | 72.99
| TransEncAsp | 77.10 | 57.92 | 86.97 | 48.96 | 65.83 | 59.53 | 74.31 | 43.20
| TransEncAsp+SCAPT | 83.39 | 74.53 | 88.04 | 68.55 | 77.17 | 73.23 | 78.70 | 72.82
| BERT-SPC† | 85.09 | 77.19 | 91.68 | 64.04 | 77.90 | 73.50 | 80.99 | 69.71
| BERT-SPC† (Aug4) | 84.20 | 76.55 | 90.50 | 64.04 | 76.65 | 70.86 | 81.64 | 63.43
| BERT-SPC† (Aug8) | 80.98 | 67.77 | 90.39 | 50.94 | 75.71 | 71.62 | 77.97 | 69.71
BERT | BERT-SPC† (Aug16) | 77.59 | 67.44 | 85.35 | 52.81 | 74.61 | 69.92 | 77.97 | 65.71
Ours | ISAIV | 87.05 | 81.40 | 92.50 | 69.66 | 80.41 | 77.25 | 81.21 | 78.29
4 |  |  |  |  |  |  |  |  |

###  4.1 Datasets and Metrics

#### Implicit Sentiment Analysis

To show our model’s better performance in understanding implicit sentiment, we
evaluate the implicit polarity prediction on a total implicit dataset,
CLIPEval from SemEval 2015 task 9 Russo et al. (2015), which consists of self-
reported entity reviews collected from psychological research with 1,280
sentences for the training and 371 for the test.

#### Aspect-based Implicit Sentiment Analysis

As our aim to dismiss the spurious correlation between explicit sentiment
words and polarity, we also conducted experiments on both explicit and
implicit datasets, Laptop and Restaurant review from SemEval 2014 task 4
Pontiki et al. (2014). The segmentation of explicit sentiment (ESE) and
implicit sentiment (ISE) is based on the work of Li et al. (2021b) based on
the annotation of opinion words Fan et al. (2019).

We adopt two widely used metrics accuracy and macro-F1 to evaluate the
performance of our model and the baselines.

###  4.2 Baselines

To investigate the effectiveness of our ISAIV model, we compare it with
several typical baseline models for implicit sentiment analysis and aspect-
based implicit sentiment analysis.

#### Implicit Sentiment Analysis

We select four popular baselines for implicit sentiment analysis, which are
listed as follows. SHELIFBK Dragoni (2015), ATTLSTM Lin et al. (2017), MTL
Zheng et al. (2018), BERT-SPC Xu et al. (2019).

#### Aspect-based Implicit Sentiment Analysis

The commonly used baselines can be split into three parts, neural network,
graph neural network, and BERT-based models, which are given as follows.

Neural Network: ATAE-LSTM Wang et al. (2016), IAN Ma et al. (2017), RAM Chen
et al. (2017), MGAN Fan et al. (2018).

Graph Neural Network: TransCap Chen and Qian (2019), ASGCN Zhang et al.
(2019), BiGCN Zhang and Qian (2020), CDT Sun et al. (2019), RGAT Wang et al.
(2020).

BERT-based Models: BERT-SPC Xu et al. (2019), CapsNet+BERT Jiang et al.
(2019), BERT-ADA Rietzler et al. (2020), R-GAT+BERT Wang et al. (2020),
TransEncAsp Li et al. (2021b), TransEncAsp+SCAPT Li et al. (2021b).

Moreover, to explore the influence of the augmentation sentences, we add them
into the training dataset for our basic model (BERT-SPC). For example, BERT-
SPC (Aug4) means we add four augmentation samples for each example.

###  4.3 Implementation Details

We implement ISAIV with PyTorch based on Hugging Face Transformer
222https://huggingface.co/bert-base-uncased. and run them on the GPU(NVIDIA
GTX 2080ti). During training, we set the coefficient $\lambda$ of
$\mathcal{L}_{2}$ regularization item is 0.01, $10^{-5}$ and dropout rate is
0.1. The learning rate is set as 2e-5 and the batch size is set as 16. Adam
optimizer Kingma and Ba (2014) is used to update all the parameters.

##  5 Experimental Analysis

Figure 4: Some examples of case studies.

###  5.1 Main Results

To evaluate the performance of our ISAIV model, we compare it with several
mainstream baseline models for both the implicit sentiment analysis and
aspect-based implicit sentiment analysis (Table 3 and Table 4). We find the
following observation from these tables. First, our model outperforms all the
baselines in most cases. Particularly, our model obtains the best F1 scores
over all the three datasets of two tasks. Second, our ISAIV strategy
significantly improves the performance of the baseline. ISAIV improves more
than two points in terms of F1 over all the datasets compared with BERT-SPC,
which is the base of our model. Third, our model can improve the performance
of implicit sentiment analysis effectively. For example, compared with the
BERT-SPC†, we obtain more than five points improvement on ISE over both
Restaurant and Laptop. Also, we obtain the best results of implicit sentiment
analysis over CLIPEval. Forth, the model that regards the augmentation
sentence as a data augmentation (e.g., BERT-SPC† (Aug4)) performs even worse
than the one without augmentation as noise may exist. This shows that our
ISAIV algorithm can help learn the implicit sentiment reasoning behind the
data.

Table 3: The main results for implicit sentiment analysis. We use the results reported in Xiang et al. (2021). The baselines with † are our implementation. 4 Method | CLIPEval
---|---
Acc | F1
SHELLFBK | 56.00 | 54.00
ATTLSTM | 82.43 | 82.21
MTL | 82.94 | 83.17
BERT-SPC† | 87.06 | 84.74
BERT-SPC† (Aug4) | 85.71 | 83.56
BERT-SPC† (Aug8) | 86.52 | 85.30
BERT-SPC† (Aug16) | 85.44 | 84.54
ISAIV | 88.95 | 87.49
4 |  |

###  5.2 Case Studies

We present five samples in Figure 4 to explain the four main types of
confounders (Section 3.1), which shows the effectiveness and rationality of
our model to reason implicit sentiment. (1) Inter-aspect Confounding Word. In
example E1, “definitely good” is the sentiment words of aspect food, implying
positive sentiment but confounds the prediction of aspect price. In E2, the
user expresses a negative sentiment towards aspect finger swipes with opinion
word “fancy”, which confounds the prediction of aspect mouse. (2) Inter-clause
Confounding Word. In E3, the first and second clauses form an adversative
relation, and the true meaning of the expression is that the mouse works well,
but the sentiment word “a little concerned” in the first clause confounds the
prediction. (3) Rhetoric Confounding Word. In E4, the customer used the
rhetorical device of a rhetorical question to express that the restaurant’s
service was terrible, but the existence of the word “hope” confounds the
prediction, (4) Dynamic Neural Confounding Word. In E5, the word “melted” is
absolutely a neutral word, but when we directly count the proportion of
aspect-level sentiment polarity that co-occur with “melted”, we surprisingly
find that $83.33\%$ aspect polarity is negative, which well explains why the
previous model predicts “negative” strangely. Due to the unbalanced
distribution of training data, the model tends to tag the neural word with
specific sentiment polarity and predict based on this spurious correlation
learned superficially before.

Table 4: The influence of augmentation sample number. 4 #Num | Restaurant 0.6 | Laptop 0.4
---|---|---
Acc | F1 | ESE | ISE | Acc | F1 | ESE | ISE
4 | 86.88 | 80.99 | 92.50 | 68.91 | 80.41 | 77.25 | 81.21 | 78.29
8 | 87.05 | 81.40 | 92.50 | 69.66 | 78.68 | 75.23 | 79.70 | 76.00
16 | 85.09 | 77.75 | 91.21 | 65.54 | 78.06 | 75.04 | 79.05 | 75.43
4 |  |  |  |  |  |  |  |

(a) Laptop

(b) CLIPEval

Figure 5: The influence of $\beta$

###  5.3 Further analysis

#### Influence of Augmentation Sample Number.

We explore the influence of augmentation sample number here (Table 4). The
influence of sample number on model performance depends on two conflicting
factors: the degree of deviation from the original sentence and the chance to
find more potential confounders. With the increase in sample number, the model
has a greater chance of finding more potential confounders and adjusting for
them. On the other hand, a larger generation samples number means that more
samples deviating from the original sentence are involved in the learning
procedure, and therefore the accuracy of prediction decreases. Over
Restaurant, the two conflicting factors reach a better balance at 8; while on
Laptop, the negative effect of semantic deviation outweighs the positive
effect of correction for more confounders, and the best result is achieved at
4.

#### Influence of $\beta$.

Either emphasizing sentiment words only or completely ignoring them is not
reasonable. The purpose of our hyper-parameter beta is to strike a balance
between these two terms (Figure 5). In Laptop and CLIPEval, performance is
best at 0.4 and 0.3 relatively, and both show a trend of high in the middle
and low on both sides, indicating that our hypothesis is rational.

#### Robustness.

We also analyze the robustness of our proposed ISAIV (Table 5). We test our
model on a robustness testing dataset, Revnon of TextFlint Wang et al. (2021),
which reverses the sentiment of the non-target aspects with originally the
same sentiment as target. Our model outperforms the model BERT-SPC without
causal intervention, which means ISAIV can also improve the robustness by
learning the implicit sentiment reasoning.

#### Limitation.

We also analyze wrong samples and find the model may fail when encountering
the expression with unusual knowledge. In Figure 4 E6, due to the lack of
prior knowledge about “gratuity", “automatically added" is likely to be
perceived as a good thing. Admittedly, our work mainly focuses on the
reasoning ability and doesn’t integrate external corpus and knowledge and
therefore lacks abundant prior knowledge. The better combination of prior
knowledge and causal inference is also an intriguing and worth exploring
field.

Table 5: The results of robustness. 4 Model | Restaurant (Trans.) | Laptop (Trans.)
---|---|---
Acc | F1 | Acc | F1
BERT-SPC | 57.04 | 44.43 | 51.05 | 41.01
ISAIV | 58.77 | 48.56 | 59.45 | 43.24
4 |  |  |  |

##  6 Related Work

###  6.1 Implicit Sentiment Analysis

Implicit sentiment analysis (ISA) task plays an important role in sentiment
analysis field Liu (2012); Zhou et al. (2019, 2020c). Early studies mainly
trained machine learning models based on hand-crafted features or explicit
characterization of implicit feature information. Some studies argued that
seemingly neural words actually contain emotional content and then construct a
lexicon Feng et al. (2013); Castelló and Stede (2017). Label propagation was
used to judge the affective polarity of the words automatically Ding and
Riloff (2016); Li et al. (2021a). Moreover, Balahur et al. (2011) proposed to
build a commonsense knowledge base (EmotiNet) with the concept of affective
value and the sentiment.

Recent efforts He et al. (2018); Tang et al. (2020) used syntax information
from dependency trees to enhance attention-based models. Using syntactic
analysis tree and CNN, Liao et al. (2019) analyzed fact-implied implicit
sentiment by fusing multi-level semantic information, including sentiment
target, sentence, and context semantic. A lot of works Zhang et al. (2019);
Sun et al. (2019); Wang et al. (2020) incorporated tree-structured syntactic
information via graph neural networks to capture aspect-aware information in
text. Another method is to utilize external corpus and pre-trained knowledge
to enhance semantic awareness of models Xu et al. (2019); Rietzler et al.
(2020); Dai et al. (2021); Li et al. (2021b); Zhou et al. (2020b).

The existing methods mainly improve the ISA by integrating external corpus and
knowledge. However, the knowledge is always not complete which will influence
the models’ performance. In this paper, we solve it via causal intervention to
learn the reasoning behind the sentiment classification.

###  6.2 Causality for NLP

Recently, some researchers are beginning to combine causality and NLP to
create more robust and interpretable models Wood-Doughty et al. (2018); Tang
et al. (2021). Most of the papers integrated backdoor and counterfactual into
NLP tasks. Particularly, Landeiro and Culotta (2016) applied the back-door
adjustment to text classification by controlling the artificially
predetermined confounding variable. Feng et al. (2021) introduced
counterfactual reasoning into the model learning process by generating
representative counterfactual samples and comparing the counterfactual and
factual samples. Veitch et al. (2021) utilized distinct regularization schema
for distinct causal structure to induce counterfactual invariance. Niu et al.
(2021) utilized the counterfactual inference on VQA models by subtracting the
language bias as direct language effect from the total causal effect.

Different from these studies, we explore the causal graph for ISA and
incorporate it using the causal intervention.

##  7 Conclusion

In this paper, we proposed a causal intervention model for implicit sentiment
analysis using instrument variable (ISAIV). Given that the current model
indiscriminately focuses on the correlation between sentiment and sentiment
words and consequently performs poorly in implicit sentiment analysis as the
explicit sentiment words disappear, we rethink the implicit sentiment analysis
from a causal perspective and analyze the four main forms of sentiment words
as potential confounders. Inspired by the instrumental variable of causal
intervention, we adopt stochastic perturbation as instrumental variable and
construct a model with two-stage learning. Across three different datasets,
including general implicit sentiment analysis and aspect-based sentiment
analysis, our ISAIV shows great advantages in implicit sentiment.

## Acknowledge

The authors wish to thank the reviewers for their helpful comments and
suggestions. This work was partially National Natural Science Foundation of
China (No. 61976056, 62076069), Shanghai Municipal Science and Technology
Major Project (No.2021SHZDZX0103).

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