# P5: Plug-and-Play Persona Prompting for Personalized Response Selection

Joosung Lee  NAVER  Minsik Oh  Donghun Lee  Kakao Brain

###### Abstract

The use of persona-grounded retrieval-based chatbots is crucial for
personalized conversations, but there are several challenges that need to be
addressed. 1) In general, collecting persona-grounded corpus is very
expensive. 2) The chatbot system does not always respond in consideration of
persona at real applications. To address these challenges, we propose a plug-
and-play persona prompting method. Our system can function as a standard open-
domain chatbot if persona information is not available. We demonstrate that
this approach performs well in the zero-shot setting, which reduces the
dependence on persona-ground training data. This makes it easier to expand the
system to other languages without the need to build a persona-grounded corpus.
Additionally, our model can be fine-tuned for even better performance. In our
experiments, the zero-shot model improved the standard model by 7.71 and 1.04
points in the original persona and revised persona, respectively. The fine-
tuned model improved the previous state-of-the-art system by 1.95 and 3.39
points in the original persona and revised persona, respectively. To the best
of our knowledge, this is the first attempt to solve the problem of
personalized response selection using prompt sequences. Our code is available
on github 111https://github.com/rungjoo/plug-and-play-prompt-persona.

##  1 Introduction

Designing a system that naturally communicates with humans is of great
interest to researchers and is widely applied to services such as Apple Siri
and Amazon Alexa. One of the critical algorithms of these services is multi-
turn response selection, which selects the most appropriate response among
many response candidates. Selecting a personalized response with a customized
chatbot is necessary for a more human-like conversational system. Indeed,
Zhang et al. (2018a) shows that dialog context alone is insufficient for
response selection.

Zhang et al. (2018a) released PERSONA-CHAT where the speakers have each
persona. The persona is expressed in multiple sentences, and they get to know
each other through conversation. PERSONA-CHAT can be used for research on
personalized response generation and selection. However, the following
challenges exist in developing personalized response selection for a real
application. 1) Building conversations based on Persona is very expensive.
PERSONA-CHAT is data from the research environment in English, and persona-
grounded corpus in other languages is difficult to access. That is, there is a
challenge to build data for real applications. 2) In general domains, the
persona may not need to be reflected. However, since previous approaches Gu et
al. (2019); Hua et al. (2020); Zhu et al. (2021); Gu et al. (2020b, 2021); Xu
et al. (2022); Das et al. (2022) are trained in combination with personas, the
model can select a response only given a persona. Therefore, previous
approaches always have the disadvantage of reflecting personal information.
For example, when the persona is related to a favorite food, it is not helpful
knowledge when answering the other topics (i.e., weather). Ideally, a chatbot
system needs the ability to consider persona as an option while maintaining
standard response selection capabilities.

We propose P5 (Plug-and-Play Persona Prompting for Personalized Response
Selection) to solve the above challenges. First, we assume that there is no
expensive persona-based corpus. Therefore, we can train only standard response
selection models that do not consider persona. Then, we show that the standard
response selection model combined with persona prompting allows response
selection to reflect persona, which is a zero-shot inference strategy. Persona
prompting improves the performance of standard response selection in persona-
based conversations. Also, the model uses persona prompting as optional
information because it is a plug-and-play method. If no persona is given to
the model, the model acts as a standard response selection model. So we can
optionally combine model and persona. Persona sentences to be used for
prompting are selected by measuring the similarity to the response. We use a
pre-trained model as our similarity model. Only top-$k$ persona sentences are
used in order of highest similarity score. In addition, we introduce a zero-
shot baseline SoP (Similarity of Persona) based on the similarity score.

To our best knowledge, previous studies only provide fine-tuned models. For
comparison in these same experimental settings, we show the experimental
results for fine-tuned P5. Our method further improves the performance of the
fine-tuned strategy as well as the zero-shot strategy. Fine-tuned P5 achieves
state-of-the-art, which proves that persona prompting is effective in learning
the relationship between persona and context. We evaluate our methods on
PERSONA-CHAT Zhang et al. (2018a) and Focus Jang et al. (2022). PERSONA-CHAT
provides 19 negative responses and 1 positive response for personalized
response selection. Focus is given only one positive response as response
candidates. Therefore, we build the data by sampling 19 negative candidates.

##  2 Related Work

###  2.1 Standard Response Selection

In dialog systems, retrieval-based response selection is an important module.
Earlier retrieval-based methods Hu et al. (2014); Wang et al. (2015) attempted
to select a response based on a single-turn context. However, since multi-turn
response selection is a scenario for a more realistic service, recent studies
Gu et al. (2020a); Whang et al. (2021); Han et al. (2021) focus on selecting a
response based on multi-turn context. These multi-turn response selection
models leverage pre-trained language models to reflect context. It also
improves performance by training a language model to understand conversations
through a post-training strategy or multi-task learning. These studies are
generally conducted on ubuntu Lowe et al. (2015), douban Wu et al. (2017), and
e-commerce Zhang et al. (2018b) corpus. Since these datasets are not given a
persona, we refer to relevant studies as standard response selection models.

###  2.2 Personalized Response Selection

Standard response selection models suffer from a coherent personality by being
trained on general conversations. Zhang et al. (2018a) releases PERSONA-CHAT
dataset, which is a dialogue corpus built on persona. Jang et al. (2022)
releases Focus dataset, which is a dialogue corpus built on persona and
knowledge.

Recently, many studies introduce fine-tuned models in PERSONA-CHAT:

Hua et al. (2020) proposes an approach that detects only context-related
persona. RSM-DCK (Response Selection Model that can Detect the relevant parts
of the Context and Knowledge collection) introduces context selectors and
knowledge selectors, which are soft-selection of persona through attention
weights. Gu et al. (2020b) also performs soft-selection of persona, and
iteratively referring not only between context and response representations
but also between knowledge and response representations to collect deep
matching features for scoring response candidates (FIRE: Filtering before
Iteratively REferring). Zhu et al. (2021) introduces hard-selection of
context-related persona, and shows that recent utterances in context are more
important for response selection (CSN: Content Selection Network). Gu et al.
(2021) shows that partner-persona as well as self-persona are important for
response selection. BERT-CRA (BERT with Context-Response-Aware Persona) also
achieves high performance by combining persona with BERT for context-aware-
persona. Xu et al. (2022) suggests COSPLAY (COncept Set guided PersonaLized
dialogue generation Across both partY personas), which considers both speakers
as "team". COSPLAY utilizes both self-persona and partner-persona, and
proposes a Concept Set framework with a suite of knowledge-enhanced operations
to process them such as set algebras, set expansion, and set distance. Das et
al. (2022) first learns emotion and intent classifiers respectively with
external data. Then, BERT-EmA (Emotion Aware Fusion) and BERT-P-EnA
(Entailment Aware Fusion) are learned by adding the predicted emotion and
intent of the utterance as input of BERT. These methods are based on BERT-CRA.

(a) Training phase

(b) Test phase

Figure 1: The overview architecture of our proposed P5 model

##  3 Task Definition

In the training phase, the dialogue dataset $D_{train}=\\{c,r,y\\}$ is given
where context $c=\\{u_{1},u_{2},\cdots,u_{n}\\}$, $u_{i}$ is the $i$th
utterance, $r$ is a response candidate, and $y\in\\{0,1\\}$ is a label. $y$ =
1 indicates a positive response, the opposite is $y$ = 0. In the test phase,
the dialogue dataset $D_{test}=\\{c,r,p,y\\}$ is given, where persona
$p=\\{p_{1},p_{2},\cdots,p_{m}\\}$ are $m$ persona sentences. Our goal is to
learn a matching model without persona from $D_{train}$ and select an
appropriate response by reflecting persona from $D_{test}$.

##  4 Approach

Figure 1 shows the proposed approach. In the training phase, the model is
trained to perform a multi-turn response selection task with $D_{train}$
similar to Gu et al. (2020a); Han et al. (2021), which is called standard
response selection. Since our goal is not to improve the performance of
standard response selection, we do not use any particular strategy (i.e.,
post-training). The test phase consists of two steps. The first step is
persona grounding corresponding to the response. The second step is
calculating the matching score of $(c,r,p)$ with the persona-prompted standard
response selection model. Our approach improves the performance by combining
the persona prompting with the standard response selection not trained with
persona, which can also be utilized as a fine-tuned method.

###  4.1 Standard Response Selection (SRS)

The standard response selection model is trained with $D_{train}$ without a
persona. The standard response selection model follows pre-trained language
models such as BERT Devlin et al. (2019) and RoBERTa Liu et al. (2019), which
are Transformer encoders Vaswani et al. (2017). We use RoBERTa as the backbone
and train with binary classification for multi-turn response selection. The
input format ($x_{st}$) is as follows:

$x_{st}=\text{concat}([SEP],u_{1},[SEP],u_{2},\cdots,\\\ [CLS],[SEP],r)$ (1)

where $u_{i}$ is $i$th utterance, [SEP] is a special token to distinguish each
utterance, and [CLS] is prepended before the response. Most studies using pre-
trained language models for response selection tasks prepend [CLS] to the
front of the entire input sequence. However, since the distance between [CLS]
and the response changes dynamically, an additional special token must be used
to inform which response to classify. We intuitively change the position of
[CLS] to represent the input as Equation 1.

The response score using the standard response selection model is calculated
as follows:

$s_{st}=W(\text{PLM}(x_{st}))$ (2)

where PLM is a pre-trained language model used in the standard response
selection model, and $W$ is a matrix that projects the output vector of [CLS]
into a two-dimensional vector. $s_{st}$ is the score vector corresponding to
$x_{st}$.

The standard response selection model is trained to minimize cross-entropy
loss:

$L=\frac{1}{N}\sum^{N}_{j=1}\text{CE}(s_{st}^{j},y^{j})$ (3)

where $j$ means the $j$th training sample, and $N$ is the number of training
data.

the number of (#) | PERSON-CHAT | Focus
---|---|---
Train | Val | Test | Train | Val
# conversations | 8,939 | 1,000 | 968 | 12,484 | 1,000
# turns | 65,719 | 7,801 | 7,512 | 70,332 | 5,639

Table 1: Statistics of the two datasets

###  4.2 Persona Grounding

Zhao et al. (2019); Gu et al. (2019); Hua et al. (2020); Gu et al. (2021) use
all given persona sentences as an input regardless of context and response.
Instead, these approaches perform soft-selection of a persona by assigning
attention weights between persona sentences and context embeddings. In other
words, small weights are assigned to less relevant persona sentences,
affecting response selection less.

Gu et al. (2020b); Zhu et al. (2021) mention that assigning a low weight to a
less relevant persona is possible, but the cumulative weight of an irrelevant
persona can be significant. Therefore, persona sentences used through the
threshold are hard-selected based on the attention weight, which is an
essential key for the model. However, since the hard-selection method extracts
feature vectors through attention between persona and context, persona
sentences are essential inputs for the trained model. That is, the previous
frameworks require at least one persona sentence, which is different from
selectively combining persona sentences like in our framework. Also, the hard-
selection method requires training data, and our persona grounding is
sufficient without training data.

We introduce an approach to select only the top-$k$ persona sentences simply
and efficiently before combining the standard response selection model and
persona in the test phase. A personalized response contains relevant personal
information. Therefore, we find the persona sentence related to the response
through the similarity between the response and the persona sentence.

$\displaystyle e_{r}$ $\displaystyle=\text{SimModel}([CLS],r)$ (4)
$\displaystyle e_{p_{i}}$ $\displaystyle=\text{SimModel}([CLS],p_{i})$ (5)
$\displaystyle s_{rp_{i}}$ $\displaystyle=\frac{e_{r}\cdot
e_{p_{i}}}{||e_{r}||\cdot||e_{p_{i}}||}$ (6)

where $r$ is the response, $p_{i}$ is the $i$th persona sentence, $e_{r}$ and
$e_{p_{i}}$ are the output vectors of [CLS] passed through the similarity
model, and $s_{rp_{i}}$ is the similarity score between the two vectors.

A persona sentence with a high similarity score ($s_{rp_{i}}$) is considered
to help select the corresponding response. Therefore, we combine only the
top-$k$ persona sentences with high similarity scores with the standard
response selection model. In our default setting, $k$ = 2, but it can be set
dynamically. We used unsupervised simcse Gao et al. (2021b) and supervised
bert-nli Reimers and Gurevych (2019) as models for calculating similarity, and
the difference between the two is introduced in Section 5.5.

###  4.3 Persona Prompting

Recently, GPT-3 Brown et al. (2020) model leverages the natural-language
prompt to improve few-shot performance very effectively. Gao et al. (2021a)
achieves high performance by prompt-based fine-tuning of small language models
on a small number of training data, which is a more practical scenario. Han et
al. (2022) introduces a dialog prompt that is created using several utterances
of fictional characters, in which a pre-trained language model that is not
trained in character styles generates attractive responses that mimic the
characters.

Inspired by these studies, we propose a persona prompting for personalized
response selection. The prompt sequence asks and answers the speaker’s
persona, simply composed of a prompt question and persona sentences. So the
input format ($x_{p}$) is as follows:

$x_{p}=\text{concat}([SEP],p_{q},[SEP],p_{1},p_{2},\cdots,\\\
[SEP],u_{1},[SEP],u_{2},\cdots,[CLS],[SEP],r)$ (7)

where $p_{q}$ is a prompt question, which defaults to "what is your
personality?" sentences are used and $p_{i}$ ($i\in\\{1,...,k\\}$) are
grounded persona sentences that result from Section 4.2. Other prompt
questions are described in Section 5.6.

In Equation 2, the input is changed to $x_{p}$, and the response score is
calculated as:

$s_{p}=W(\text{PLM}(x_{p}))$ (8)

where $s_{p}$ is the score vector of the response considering persona and
context. The response selection model does not learn about persona fusion but
naturally recognizes the prompt sequence as part of the context.

###  4.4 Baseline: Similarity of Persona (SoP)

In the previous approaches, only fine-tuned approaches have been studied under
the assumption that persona is given. Therefore, we introduce a baseline for a
simple zero-shot setting, which utilizes a similarity score used to find a
persona related to a response. That is, the final response score is the
weighted sum of the response score of the standard response selection model
and the similarity scores.

$s_{f}=s_{st}+\alpha F(s_{rp_{1}},\cdots,s_{rp_{m}})$ (9)

where $s_{rp_{i}}$ is obtained from Equation 6 as a similarity score between
the $i$th persona sentence and the response, and $s_{st}$ is obtained from
Equation 2 as a score using the standard response selection model, and $s_{f}$
is the final score. $F$ is the function to aggregate $s_{rp_{i}}$ and $\alpha$
is the weight. We tried top-$k$ average function for $F$, but it was most
effective to use top-1 $s_{rp_{i}}$. Therefore, the max function is used for
$F$ in the experiment.

##  5 Experiments

###  5.1 Datasets

We experiment on two benchmark datasets. Table 1 shows the statistics of the
datasets, and Table 6, 7 in Appendix A are examples.

PERSONA-CHAT The first dataset is PERSONA-CHAT, where each speaker is
described with multiple persona sentences. PERSONA-CHAT is a dataset mainly
used in previous studies, and 1 positive response and 19 negative response
candidates corresponding to the context. Response selection is performed for
every turn of dialogue. PERSONA-CHAT provides two versions of persona,
original and revised. The revised persona is data that makes the task more
difficult by rephrasing the original persona.

Focus The second dataset is Focus, a dialogue created using persona and
knowledge. Focus was created from the motivation that more appropriate
utterances can be generated by considering persona and knowledge together.
However, for personalized response selection, we only use persona. Since only
a positive response is given to the context in Focus, 19 negative response
candidates are sampled and formatted according to the response selection task.
The sampling strategy follows two steps. (1) Context sampling from the
speaker’s previous utterances. In this case, utterances using the same persona
sentence are sampled first. (2) Random sampling from the corpus. In (1), 2
candidates are sampled, and in (2), 17 candidates are sampled. Therefore,
models can achieve high performance by considering both the appropriateness of
response and persona in Focus. Also, Focus has a label for persona grounding
when constructing a positive response, so it is used to measure the
performance of our proposed persona grounding. There can be multiple persona
sentences labeled as "True".

Method | Model | Original Persona | Revised Persona | Focus
---|---|---|---|---
| RSM-DCK Hua et al. (2020) | 79.65 | 71.85 |
| FIRE Gu et al. (2020b) | 81.6 | 74.8 |
| CSN-word Zhu et al. (2021) | 78.1 | 70.1 |
| BERT-CRA Gu et al. (2021) | 84.3 | 79.4 |
| COSPLAY Xu et al. (2022) | 85.5 | 74.4 |
| BERT-EmA Das et al. (2022) | 84.6 | 79.8 |
| BERT-P-EnA Das et al. (2022) | 85.3 | 80.5 |
Fine-tuning | P5 | 87.45 | 82.79 | -
| SRS | 72.4 | 72.4 | 91.65
| SoP | 76.41 | 73.16 | 94.27
Zero-shot | P5 | 80.11 | 73.44 | 97.85

Table 2: Evaluation results on the test sets of PERSONA-CHAT and validation
sets of Focus. Performance is measured as $R@1$. Bold text indicates the best
performance in each part. In PERSONA-CHAT and Focus, RoBERTa-base are used as
PLM.

###  5.2 Evaluation Metric

We use the evaluation metric used in previous works Gu et al. (2021, 2020b)
for a fair comparison. Each model checks whether the candidate with the
highest ranking score is a positive response, denoted by $R@1$. Specifically,
both PERSONA-CHAT and Focus are $R_{20}@1$ because 1 positive response and 19
negative responses are given as candidates.

###  5.3 Training Setup

We use a pre-trained model from the huggingface library
222https://huggingface.co/docs/transformers/index. For the standard response
selection model, we use AdamW as the optimizer. The learning rate is an
initial value of 1e-6, and get_linear_schedule_with_warmup provided by the
huggingface library is used for the learning rate scheduler. The maximum value
of 10 is used for the gradient clipping. The training epoch is 10, the model
is evaluated on the validation data for each epoch, and the best model is
selected. In prompt-based fine-tuning, the training epoch is 5, and the rest
are the same as the standard response selection. All experiments are performed
on one A100 GPU, and the results are for a single turn because there is little
variation between each run.

###  5.4 Results

Table 2 shows the evaluation results of the previous and the proposed method
for two benchmarks. Previous methods are described in Section 2.2. SRS is a
standard response selection model that does not consider persona. SoP is a
baseline using the similarity score of persona introduced in Section 4.4. P5
is our proposed method using persona prompting.

SRS does not consider persona but achieves a performance of 72.4 in PERSONA-
CHAT, which is an unsatisfactory performance in the original persona, but a
good performance in the revised persona. We believe that many previous models
(RSM-DCK, FIRE, CSN-word, COSPLAY) were effective at fusing the original
persona, but did not fuse the revised persona well.

SoP is a simple baseline we propose, which improves the performance of SRS. In
Equation 9, $\alpha$ is 0.5 and 0.05 in the original persona (or Focus) and
revised persona, respectively, and an appropriate value for $\alpha$ was
selected through an experiment. Since the original persona has many examples
that directly overlap the words of the response, the similarity score is more
effective. However, since a revised persona is a rephrased sentence, simply
scoring similarity with persona sentences is less effective.

P5 identifies the speaker’s persona from the prompt in the form of simple
dialogue and uses it for response ranking. P5 achieves the best performance in
both zero-shot and fine-tuning. Zero-shot P5 improves the performance of the
SRS through persona prompting by 7.71 points in the original persona and 1.04
points in the revised persona. The zero-shot inference strategy is more
effective for the original persona (or Focus) than the revised persona, which
is considered difficult for the SRS to understand the revised persona as a
prompt context. When a persona is given as training data, the fine-tuned P5
achieves 87.45 and 82.79 performance in the original persona and revised
persona, respectively, which is a remarkable performance improvement compared
to previous models. Also, our proposed prompt is a plug-and-play module and
has the advantage of being turned on and off according to the real
application.

We further experimented with the zero-shot setting in Focus to verify the
effectiveness of our model. Focus is structured similarly to the original
persona in PERSONA-CHAT, and the experimental results also show the same
aspect as the original persona. Since zero-shot P5 has already achieved
satisfactory performance in Focus, fine-tuned P5 has not been tested.

Figure 2: Performance of similarity model on persona grounding in Focus

Model | prompt question | similarity model | order of persona | Original Persona | Revised Persona
---|---|---|---|---|---
Zero-shot P5 | what is your personality? | simcse | ascending | 80.11 | 73.44
tell me your personality. | simcse | ascending | 79.73 | 73.34
tell me more about yourself. | simcse | ascending | 79.81 | 73.32
random utterance | simcse | ascending | 78.89 | 72.63
empty string | simcse | ascending | 79.71 | 73.31
what is your personality? | nli | ascending | 79.86 | 73.4
what is your personality? | simcse | descending | 79.7 | 73.34

Table 3: Performance with variants of persona prompting

###  5.5 Effects of Persona Grounding

With a persona format similar to the original persona in PERSONA-CHAT, Focus
provides a persona grounding label. Figure 2 shows the performance of persona
grounding using simcse and bert-nli models in Focus, where both simcse and
bert-nli models are pre-trained models. The evaluation metric is $R@k$, where
the value is considered 100 if the number of "True" candidates is 0. As $k$
increases, $R@k$ improves, which increases the probability that persona
sentences related to the response are reflected. However, as $k$ increases,
irrelevant persona sentences are also entered as input. Therefore, an
appropriate value of $k$ is required.

Figure 3: Performance of zero-shot P5 change according to the number of
persona sentences used

In Table 2, P5 is the evaluation results using only 2 persona sentences
(top-2). Since PERSONA-CHAT does not have a label for persona grounding, we
cannot confirm the ground truth persona sentences reflected in the response.
Figure 3 is zero-shot P5 performance change according to the number of persona
sentences used in PERSONA-CHAT. The best performance is achieved when two
persona sentences are used in both the original persona and the revised
persona. More persona sentences increase the persona grounding performance but
confuse the personality to be reflected by the model and degrade the
performance. Simcse has a higher rate of finding used persona sentences than
the bert-nli model, and similar results can be expected in PERSONA-CHAT.
Comparing the 1st and 6th rows in Table 3, it can be seen that simcse is more
effective than bert-nli in PERSONA-CHAT.

###  5.6 Structure of Persona Prompting

Table 3 shows zero-shot P5 performance for the variant of persona prompting.
We changed the prompt question "what is your personality?" to the rephrase
"tell me your personality." and "tell me more about yourself.". The
performance difference according to the prompt question is not large, and it
is not easy to find the optimal prompt question in the discrete space.

The random utterance (4th row) indicates that the prompt question was randomly
sampled from the training utterances. The empty string (5th row) indicates
that there is no prompt question, which means that the prompt sequence
consists only of persona sentences. These two methods make it difficult to
know whether the persona sentences represent the speaker’s personality.
Performance is slightly lower than when the prompt question is "what is your
personality?", but it doesn’t show a huge difference. That is, persona
sentences are more important to performance than prompt questions.

We also experiment with two methods for the input sequence of a grounded
persona. The first is the ascending method, from lowest to highest similarity
score, which means that the position of the most similar persona and response
is close (1st row). The second is the descending method, from highest to
lowest similarity score, which means that the position of the most similar
persona and response is far (7th row). Experimental results show that the
ascending method achieves higher performance. Therefore, the closer the
distance between the relevant persona and the response, the more effective it
is for the model.

###  5.7 Other Standard Response Selection Model

Table 4 shows the results for another backbone in PERSONA-CHAT. We
experimented by changing the backbone of the SRS from RoBERTa-base to RoBERTa-
large. The performance of the SRS-large is better than that of the SRS-base.
Zero-shot P5-large improves the performance by 7.17 and 1.65 points,
respectively, in the original persona and revised persona compared to SRS.
Regardless of the performance of the SRS, the P5 effectively combines persona
to improve performance. Table 8 in Appendix B show experiments on more diverse
backbones.

Fine-tuned P5-base achieves the best performance, but fine-tuned P5-large
achieves SoTA with a larger margin. In addition, zero-shot P5-large achieves
competitive performance with previous fine-tuning approaches. That is, with
better SRS, we observe that even the zero-shot approach can achieve remarkable
performance.

Method | Model (large) | Original Persona | Revised Persona
---|---|---|---
Fine-tuning | P5 | 90.54 | 86.62
Zero-shot | SRS | 77.96 | 77.96
P5 | 85.13 | 79.61

Table 4: Experimental results for a large model.

###  5.8 Ablation Study

We perform several ablation studies when testing to know the importance of
each part of the framework. Without persona grounding (G), P5 considers the
personality to be all persona sentences, and the order is randomized to form
the prompt sequence. This is similar to the experiment in Figure 3 where the
number of persona = 5. Without prompt question (Q), P5 uses only persona
sentences as the prompt sequence, which is the same as the 5th row in Table 3.
Without a prompt sequence (P), P5 does not consider persona as context, which
is equivalent to standard response selection. -D indicates that SRS does not
have access to PERSONA-CHAT dialogue, so it is trained with external data. We
use dailydialog Li et al. (2017) as training data and 10 negative candidate
responses are randomly sampled.

In the zero-shot setting, ablation studies are performed on persona grounding,
prompt question, prompt sequence (prompt question+persona sentence), and
dialogue corpus. All components clearly show differences in the original
persona. The absence of persona grounding and prompt questions reduces
performance, but these are considered minor components. However, persona
sentences are an important component of performance, and using them as prompt
sequences is our major contribution. We also assumed that the model is
inaccessible to dialogue as well as persona sentences from PERSONA-CHAT. So
SRS is trained as dailydialog. Zero-shot P5 (-D&P) (i.e. SRS w/ dailydailog),
without using persona sentences in the test, achieves a performance of 44.5.
Zero-shot P5 (-D) utilizing persona sentences achieves the performance of
59.94 and 50.23 in the original persona and revised persona, respectively,
which is a much larger performance improvement than shown in Table 2. With the
same conclusion as in Section 5.7, the proposed prompting proves to lead to a
large performance improvement regardless of SRS.

In the fine-tuning setting, ablation studies are performed on persona
grounding, prompt sequences when testing. Fine-tuning P5 (-G) achieves 87.13
and 81.55 performance on the original and revised, respectively, showing that
the performance difference due to persona grounding is smaller than the zero-
shot method. In addition, it has the advantage of not requiring additional
computation for persona grounding. This is because the model gains the ability
to attend to the appropriate persona when selecting a response through
learning from the persona corpus. Therefore, our prompting method operates
effectively in a fair comparison with other frameworks. Fine-tuning P5 (-P)
achieves a performance of 68.18 and 70.38 in the original and revised
versions, respectively, which is worse than zero-shot P5 (-P). Therefore, we
find that fine-tuning P5 exhibits a strong dependence on the persona sentence
when selecting responses. These limitations will have similar limitations to
fine-tuned models as in previous studies.

Method | Model | Original Persona | Revised Persona
---|---|---|---
| P5 | 80.11 | 73.44
| -G | 78.93 | 72.08
| -Q | 79.71 | 73.31
| -P | 72.4 | 72.4
| -D | 59.94 | 50.23
Zero-shot | -D&P | 44.5 | 44.5
| P5 | 87.45 | 82.79
| -G | 87.13 | 81.55
Fine-tuning | -P | 68.18 | 70.38

Table 5: Ablation study for each module of the framework. G stands for persona
grounding, Q stands for prompt question, P stands for prompt sequence, and D
stands for PERSONA-CHAT dialogue.

##  6 Conclusion

In this paper, we present a method called P5, which functions as a plug-and-
play system that only incorporates persona when desired. Our approach involves
identifying related persona sentences through their similarity to a given
response, and then adding these sentences as a prompt to the input. This
allows the standard response selection model to better match context and
response by taking into account the persona. We evaluate our method on two
benchmark datasets using both fine-tuning and zero-shot settings. Fine-tuned
P5 outperforms previous studies by a significant margin. Zero-shot P5 also
effectively improves performance when compared to standard response selection
models. Even the zero-shot P5-large shows performance that is comparable to
previous fine-tuning approaches.

P5 is only evaluated using persona-based corpus, however, in real-world
applications, persona information is not always available. Therefore, it is
important that the standard response selection model can be combined with
persona in a dynamic manner. One way to achieve this is by only incorporating
persona sentences that have a similarity score above a certain threshold. We
plan to investigate other options for reflecting persona in future studies.

## Limitations

P5 is only evaluated using persona-based corpus, however, in real-world
applications, persona information is not always available. Therefore, it is
important that the standard response selection model can be combined with
persona in a dynamic manner. One way to achieve this is by only incorporating
persona sentences that have a similarity score above a certain threshold. We
plan to investigate other options for reflecting persona in future studies.

The importance of a standard response selection model outweighs the use of
persona sentences in personalized response selection. In Table 5, the P5 (-D)
performance improves with persona prompting, however, it is still lower than
that of P5 (-P). The low performance of the standard response selection model
(P5 (-D&P)) is the reason for this. To improve zero-shot P5 performance, it is
crucial to improve the standard response selection performance. Therefore, we
will conduct further research on enhancing the performance of zero-shot
standard response selection models that do not utilize PERSONA-CHAT.

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##  Appendix A Dataset Example

PERSONA-CHAT has {8,939, 1,000, 968} conversations in {train, validation,
test} and Focus has {12,484, 1,000} conversations in {train, validation}.
Table 6, 7 show a example of PERSONA-CHAT and Focus, respectively.

| Pesona 1 | Persona 2
---|---|---
Original | i love to meet new people. | i work as an accountant.
my favorite sport is ultimate frisbee. | i live in ohio.
autumn is my favorite season. | i am a single mom of two boys.
my parents are living in bora bora. | i drive a honda civic.
i have a turtle named timothy. | i like to go hiking in my spare time.
Revised | i like getting friends. | they call me a bean counter.
i love to run around and get out my energy. | i am from the north.
i love watching the leaves change colors. | i am raising sons all on my own.
my family lives on a island. | i own a small car.
reptiles make good pets. | i enjoy nature walks.
Dialogue | person 1: hi , i am kera and i am a social butterfly
person 2: hi . i am more the mousy type . numbers are my world at my day job .
you ?
person 1: i work for a tech firm , i am a tom girl
person 2: i am just an ohio mom with two amazing sons . not married though .
person 1: cool . i have no kids just my pet turtle timothy
person 2: great pet name . i do not have any pets unless you count my car ,
sally .

Table 6: An example from PERSONA-CHAT dataset

| Persona 2 | Persona Grouding
---|---|---
Original | I like to go to Church. | FALSE
I am Roman Catholic. | FALSE
I wish to go to El Paso. | TRUE
I would like to go to Texas. | FALSE
I love the United States. | FALSE
Dialogue | person 1: Wow, this is amazing! What is this?
person 2: It is a Church, something which you like.
person 1: What is the name of this place?
| person 2: The name of this place is The Roman Catholic Diocese
---
of El Paso, remember you are Roman Catholic.
person 1: Where is this place?
person 2: It is located in El Paso, a city which you wish to go.

Table 7: An example from Focus dataset. The label of persona grounding is for
the last utterance.

##  Appendix B Additional Standard Response Selection Model

Backbone | Method | Model | Original Persona | Revised Persona
---|---|---|---|---
|  | SRS | 72.4 | 72.4
|  | P5 | 80.11 | 73.44
|  | SRS w/ DD | 44.5 | 44.5
RoBERTa-base |  | P5 w/ DD | 59.94 | 50.23
|  | SRS | 77.96 | 77.96
RoBERTa-large |  | P5 | 85.13 | 79.61
|  | SRS | 69.61 | 69.61
BERT-base |  | P5 | 76.66 | 69.74
|  | SRS | 67.37 | 67.37
ALBERTv2-base |  | P5 | 76.58 | 68.89
|  | SRS | 69.68 | 69.68
ConvBERT-base | Zero-shot | P5 | 78.49 | 71.49

Table 8: Experimental results from all the backbones we experimented with.

We prove that the proposed prompt sequence is effective through more extensive
experiments with various backbones. In addition to RoBERTa, we experiment with
BERT Devlin et al. (2019), ALBERT Lan et al. (2020), and ConvBERT Jiang et al.
(2020) as backbones. ALBERT (A Lite BERT) is a language model for natural
language processing that was designed to be more efficient than its
predecessor, BERT. ALBERT reduces the amount of computation through matrix
decomposing through factorized embedding parameterization and uses Sentence
Order Prediction (SOP) as loss. ConvBERT is a language model for natural
language processing that combines the power of convolutional neural networks
with transformer architecture. It utilizes a hierarchical convolutional
structure to capture local dependencies and long-range dependencies, resulting
in improved computational efficiency and better performance on various
language tasks. The experimental settings are the same for all backbones.

SRS is a response selection model trained without persona sentences. P5 is a
model in which SRS receives both the prompt sequence and context as inputs and
selects a response when testing. "with DD" means that the performance was
measured based on the SRS learned by dailydialog, which is the same as -D and
-D&P in Table 5. Depending on the backbone model, the effect of improving
performance is different, but in the original persona, the model’s ability
improves by a large margin. In the revised persona, P5 performance improves
less than in the original persona. The large backbone did not differ from the
base backbone. SRS w/ DD trained with dailydialog has lower performance, but
the effect of the prompt sequence is significant.

We demonstrate that prompt sequences improve the performance of SRS on all
backbones. We believe that pre-trained language models help to perform this
zero-shot inference because they are trained from a large corpus and thus have
the ability to understand context. The prompt sequence helps to achieve higher
performance regardless of the performance of SRS, which is a very simple but
powerful method. However, the effect is relatively small in the revised
persona, so further research will be done in this area.