# Detecting Insincere Questions from Text: A Transfer Learning Approach

Ashwin Rachha<EMAIL_ADDRESS>Dept. of Computer Science
Pune Institute of Computer Technology,
Maharashtra,India
Gaurav Vanmane<EMAIL_ADDRESS>Dept. of Computer Science
Pune Institute of Computer Technology,
Maharashtra,India

###### Abstract

The internet today has become an unrivalled source of information where people
converse on content based websites such as Quora, Reddit, StackOverflow and
Twitter asking doubts and sharing knowledge with the world. A major arising
problem with such websites is the proliferation of toxic comments or instances
of insincerity wherein the users instead of maintaining a sincere motive
indulge in spreading toxic and divisive content. The straightforward course of
action in confronting this situation is detecting such content beforehand and
preventing it from subsisting online. In recent times Transfer Learning in
Natural Language Processing has seen an unprecedented growth. Today with the
existence of transformers and various state of the art innovations, a
tremendous growth has been made in various NLP domains. The introduction of
BERT has caused quite a stir in the NLP community. As mentioned, when
published, BERT dominated performance benchmarks and thereby inspired many
other authors to experiment with it and publish similar models. This led to
the development of a whole BERT-family, each member being specialized on a
different task. In this paper we solve the Insincere Questions Classification
problem by fine tuning four cutting age models viz BERT, RoBERTa, DistilBERT
and ALBERT

###### Index Terms:

Natural Language Processing, Transfer Learning, Text Classification, Natural
Language Understanding, language Modeling.

##  I Introduction

Content based websites such as Quora, Reddit, StackOverflow are primarily used
for seeking genuine answers to questions. People from different domains put up
their questions and educators or people knowledgeable in a certain field
answer them. One major impediment to a plain sailing execution of information
exchange is the proliferation of toxic comments. The key challenge is to weed
out such toxic comments termed as Insincere Questions. An Insincere Question
is designated as a comment intended to make a statement than to look for
genuine answers.

An Insincere Question is characterised by:

  * •

Having a non-neutral tone.

  * •

Being disparaging or inflammatory.

  * •

Not being grounded in reality.

  * •

Expressing explicit content.

This major class of problem pertains to Text classification which has been a
benchmark problem of evaluating various research advancements in natural
language processing. While traditional machine learning algorithms such as
naive bayes, logistic regression and decision trees can be rightfully applied
to this problem, they suffer with major impediments in their constructs.
Vanilla RNNs, Gated Recurrent Unit and Long Short Term Memory Networks
replaced their usage as the new state of the art. Even though LSTMs and GRUs
performed well, they failed to capture the dependencies in long range
sentences. Now with the advent of Transfer Learning, Language model pre-
training has proven to be useful in learning universal language
representations. Researchers in the field are developing new and better
language models at an unprecedented speed. Applying these new state of the art
models could improve current methods and replace manual labeling tasks for
text classification, but also find widespread application in similar other
fields, such as machine translation and question answering. In this paper, we
test this by applying new transformer models from the BERT-family to improve
the current method of binary text classification in the context of Insincere
Questions Classification. We make use of the Quora Insincere Questions
Classification dataset [1] for this purpose We find that all of our models
achieve remarkable results in classifying the given data (AUC’s ranging from
0.92-0.97), with BERT achieving the best results compared to RoBERTa,
DistilBERT, and ALBERT. This indicates that the models are well equipped to
take over tasks that researchers have previously solved in less optimal ways.

##  II Related Work

Detecting divisive and inappropriate content is a highly relevant task in
Natural Language Processing today. Over the last few years, the class of
problems pertaining to text classification have been dominated by deep
learning based architectures. Kim [2], reports a series of experiments with
CNN trained on top of pre trained word-vectors for sentence level
classification tasks. Liu et al [3] develop what is called the XMLCNN built on
top of KimCNN with modifications such as wider convolutional kernels, adaptive
dynamic max-pooling, and an additional bottleneck layer to capture the
features of large documents more efficiently. Yang et al [4] propose the
Hierarchical Attention Network model which consists of two levels of attention
mechanism operating at the word as well as the sentence level thus focusing
distinctively on more and less important content for deriving document
representations. Adhikari et al [5] propose a simple Bidirectional LSTM with
attention mechanism and appropriate regularization techniques to yield next to
state-of-the-art results on document classification.

Alternatively, substantial work has been done to prove pre-trained models
trained on a large corpus of data yield promising results in this domain, thus
avoiding training models from scratch. Mikolov et al [6] introduced a skip
gram model with negative sampling termed word2vec and pennington et al [7]
came up with the glove embeddings which leverages statistical information by
training only on the nonzero elements in a word-word co occurrence matrix
rather than their sparse representations. Peters et al [8] introduce Elmo
which is a deep contextualized word representation which models complex syntax
and semantics, trained with Bidirectional LSTMs on a large text corpus using a
fixed embedding for each word, looks at the entire sentence before assigning
each word in it an embedding.

More recently the attention algorithm was introduced that completely changed
the landscape in the NLP field. It was first introduced by the Google Brain
team with the paper “Attention is all you need” (Vaswani et al [9])
emphasizing the fact that their model does not use recurrent neural networks
at all. Attention had already been a known idea used in LSTMs, but this was
the first time it completely took the place of the recurrence. In the
following paragraphs we explore various models and advancements that are built
on top of the aforementioned works.

##  III Methodology

###  III-A Dataset

The train set comprises over 1.3M question texts along with their
corresponding labels. A question may be sincere or insincere. Sincere
questions are labeled 0 while insincere questions are labeled as 1. There is a
severe class imbalance as can be seen in Fig1. Values pertaining to sincere
questions comprise of a large amount while only a small fraction that is 80K
pertaining to insincere questions. The question texts and labels are extracted
out from the train set and pre-processed to further train the models upon.

Figure 1: Quora Insincere Questions Dataset

###  III-B Data Preprocessing

As in all language models, text data has to be preprocessed before it gets fed
into the model. Bert and bert based models require its inputs to be tokenized
with the BertWordPiece tokenizer which tokenizes the input sequences by
matching the words with its inbuilt vocabulary and segments an individual word
till it is not found in the vocabulary and completes the segmented part with
##. Furthermore the sequence length must be the same for each question. The
max sequence length permissible for Bert is 512 but for the sake of maximum
memory usage we use the length of 192. Sequence lengths greater than 192 are
truncated and lesser are padded with zeros. After chunking the sequences they
are encoded by the tokenizer and ready to be used.

###  III-C Model Training

Four transformer models viz. Bert, Roberta, DistilBert and Albert have been
fine tuned on the aforementioned dataset. Training was done using the python
programming language on top of the Tensorflow library and the keras framework.
The training of the models was done on Tensor Processing Units (TPUs) provided
by Kaggle. TPUs provide a better performance with respect to Tensorflow and
Keras computations on tensors against GPUs. For efficient usage of memory the
batch size was kept constant as 16 and the max length of an input sequence was
initialized to 192. This made sure that the memory was completely utilized.
For optimizing parameters of the model we used the Adam optimization function
with a learning rate of “1e-5 ” for all models. Since this problem relates to
binary classification we used the binary cross entropy loss function. The
models were trained for 3 epochs. Because of severe imbalance in the data,
along with traditional validation metrics, we included AUC scores and F1
scores for validation of the models.

##  IV Background

In this section we will see various models used for traditional natural
language processing and how the existing state of the art models are built on
top of them.

###  IV-A Recurrent Neural Networks:

Recurrent Neural Networks are a class of artificial neural networks that were
first introduced to manipulate sequential data. RNNs exhibit temporal dynamic
behavior which allow them to transfer information more easily from one time
step to another. The information from the previous states is termed as a
hidden state.

Figure 2: Architecture Of Traditional RNN

[10]

For each timestep t, the activation

$a^{<t>}=g_{1}\left(W_{aa}a^{<t-1>}+W_{ax}x^{<t>}+b_{a}\right)$

And output

$y^{<t>}=g_{2}\left(W_{ya}a^{<t>}+b_{y}\right)$

where $W_{ax}$, $W_{aa}$, $W_{ya}$, $b_{a}$, $b_{y}$ are coefficients that are
shared temporally and$g_{1}$,$g_{2}$ activation functions.

RNNs showed good results in a majority of applications ranging from time
series prediction, text generation, biological modeling, speech recognition
etc. However vanilla RNNs suffer from a major problem of vanishing gradients.
As many other machine learning algorithms, RNNs are optimized using Back
Propagation and due to their sequential nature the error decays severely as it
propagates back through layers. The gradient which is very small, thus
effectively prevents the weight from changing its value. In the worst case the
neural network may even stop training further.

###  IV-B LSTM

In order to remedy the vanishing gradients problem, Long Short Term Memory
Networks were introduced, the architecture of which consists of different
gates viz update, relevance, forget and output. This introduction of a
tweaking in the architecture of RNNs enabled LSTMs to remember relevant
context from long range dependencies and introduced the flexibility to use
long sentences.

Figure 3: Long Short-Term Memory(LSTM)

[11]

$\tilde{c}^{<t>}=\tanh\left(W_{c}\left[\Gamma_{r}\star
a^{<t-1>},x^{<t>}\right]+b_{c}\right)$
$c^{<t>}=\Gamma_{u}\star\tilde{c}^{<t>}+\Gamma_{f}\star c^{<t-1>}$
$a^{<t>}=\Gamma_{o}\star c^{<t>}$

where

$\Gamma_{u}=Updategate$ $\Gamma_{r}=relevancegate$ $\Gamma_{f}=Forgetgate$
$\Gamma_{0}=Outputgate$

Figure 4: Transformer Architecture

[9]

To boost the performance of existing architectures Bidirectional LSTMs were
introduced that can process sequences from forward as well as backward
directions intended for the network to learn better understandings from both
sequence directions. The downside to this is the model being extremely slow
due to the huge number of parameters to train. This is mitigated by the use of
Gated Recurrent Networks (GRUs) as they are a faster version of LSTMs, as they
use only two gates viz update and output.

However the problem is only partially solved. There still is a sequential path
from older cells to the current one. LSTMs even though can learn long term
information, but they have trouble remembering sequences of a thousand or
more.

###  IV-C Transformer

In the 2017 paper titled ‘Attention is all you need’ by Vaswani et al [9],
they introduce the transformer, a neural network architecture based on self-
attention that performed exceptionally well in various language understanding
tasks. Unlike RNNs, Transformers do not require the sequential data to be
processed in order. Due to this feature the advantage of the Transformer was
threefold it allowed for parallelization of tasks, resulted in simpler
operations and increased the overall performance of the model on various
tasks. The transformer model is completely reliant on the attention mechanism
which allows modeling of dependencies without concern of their distance in the
input or output sequences. Transformers forgo the use of recurrence by
entirely relying on self-attention thus enabling parallelization.

The basic building blocks of a transformer consists of an encoder and decoder
stacked with N(N=6) identical layers. The encoder maps an input sequence
(x1,…,xn) to a sequence of continuous hidden context z = (z1,..,zn). The
decoder then takes z and generates an output sequence y(y1,..,ym) one element
at a time.

Self Attention: An attention function maps a query and a set of key-value
pairs all encoded as embeddings to an output which is the weighted sum of the
values, where the weight is calculated during training by a compatibility
function of the query with the corresponding key. Here keys, values and
queries are linearly projected to perform the attention function called the
‘scaled dot-product attention’. The matrix of output is calculated as:

$\text{ Attention
}(Q,K,V)=\operatorname{softmax}\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V$

Multi Headed Attention: In the original implementation Vaswani et al found it
more beneficial to use self attention h times with different learned linear
projections to dk, dq and dv dimensions. This function is performed in
parallel yielding a dv dimensional vector. The multi-head characteristic makes
it possible to use different representation subspaces at different positions.
The multi-headed attention is applied into the transformer in three different
ways:

  1. 1.

The encoder makes use of self attention in which the encoder attends to all
positions in the previous layer of the encoder.

  2. 2.

In a similar way self attention layers in the decoder allow each position in
the decoder to attend to all positions in the decoder up to and including that
position.

  3. 3.

In encoder-decoder attention as the queries come from the previous decoder
layer and the key and values come from output of the encoder, this allows
every position in the decoder to attend over all positions in the input
sentence.

Positional Encodings: Since the model does not make use of any recurrence
layers or convolutions, it needs to have a sense of the sequence order. In
order to give the model an understanding of the absolute position of tokens in
the sequence, the input embeddings are injected with positional embeddings
with which they are summed up. They use two functions to be calculated for
each position - sine and cosine. Using these the authors create a unique
vector for each position in the sequence.

Fully Connected Feed Forward Networks: Each layer of the encoder and decoder
consists of a Fully Connected Feed Forward network in addition to the
attention layer.

##  V Models

###  V-A BERT

BERTs architecture is a multi layer bidirectional Transformer encoder which
has the base Transformer by Vaswani et al as its discrete building block. The
original paper by Devlin et al [12] proposes two models viz $BERT_{B}ase$ and
$BERT_{L}arge$ with variations with respect to number of layers, hidden size
and number of self attention heads. The $BERT_{B}ase$ is a smaller version
similar to the size of the OpenAI GPT with 12 number of layers, 768 Hidden
states and a total of 12 attention heads while $BERT_{L}arge$ comprises of 24
layers, 1024 Hidden states and 16 attention heads.

Figure 5: BERT Architecture

[12]

Pretraining Bert:  Unlike traditional language modelling algorithms which are
trained either from left-to-right or right to left, BERT is trained using two
unsupervised tasks i.e Masked Language Modeling and Next Sentence Prediction
respectively. These tasks are performed on the BookCorpus (800 million words)
and English Wikipedia (2500 million words) datasets.

Masked Language Modeling (MLM) : In order to capture deep bidirectional
representation, devlin et al let BERT predict masked words based on the
context. They arbitrarily conceal 15% of the tokens in each sequence. Of these
15%, only 80% are substituted with a [MASK] token, 10% are replaced with a
random other token and 10% are kept the same. In this case the final hidden
vectors analogous to the mask tokens are def into an output softmax over the
vocabulary.

Next Sentence Prediction (NSP) : In order to train the network to model the
relationship between sentences, Devlin et al decided to apply a Next Sentence
Prediction task. BERT has to resolve for pairs of sentence segments(each
segment can consist of numerous sentences) whether they actually succeed each
other or not. Specifically while training, when choosing two sentences, the
second sentence follows the first 50% of the times (labelled as IsNext) and
rest of the times is replaced with a random sentence for the corpus(labelled
as NotNext)

###  V-B RoBERTa

In July 2019, Liu et al [13] published a model called RoBERTa (which stands
for Robustly-optimized BERT approach). The authors saw room for improvement in
the pre-training, arguing that BERT was significantly undertrained. They
changed some pre-training configurations, and RoBERTa outperformed BERT on the
GLUE benchmark. Since RoBERTa was developed based on BERT, the two models
share the transformer architecture. However, Liu et al. examined some of
BERT’s pre-training settings in an experimental setup and subsequently decided
to implement the following changes:

  1. 1.

Adding extra Training Data: As BERT model was trained on the BookCorpus and
English Wikipedia with the total size of training being 16GB, for RoBERTa Liu
et al used those two datasets along with three additional sources i.e CC-News,
Stories and the OpenWebText dataset making the training data tenfold of the
original training data.

  2. 2.

Dynamic Masking Pattern: BERT’s masking approach relied on performing masking
once during data pre-processing, which resulted in one static mask. Liu et al.
adjusted this method when trying different versions of masking for RoBERTa:
first, they duplicated the data 10 times over 40 epochs of training. This
resulted in four masks, where each training sequence was seen only four times
during training procedure (once per mask) instead of every time within one
mask. Then, the authors also compared this approach with dynamic masking,
where the mask is generated every time the sequence is passed to the model.

  3. 3.

Removing Next Sentence Prediction Objective : Removing the NSP loss led to
improvements while testing on four different tasks. Doc-Sentences performed
best, but resulted in an increased batch size and less comparability -
therefore, Liu et al. decided to use Full-Sentences in the remainder of their
experiments.

  4. 4.

Training with larger batches :  The original BERT (BASE) was trained over 1
million steps with a batch size of 256 sequences. Liu et al. note that
training over fewer steps with an increased batch size would be equivalent in
terms of computational costs. Experimenting with those settings, they find out
that training with bigger batches over fewer steps can indeed improve
perplexity for the masked language modeling pattern and also the final
accuracy.

###  V-C DistilBERT

Sanh et al [14] introduce the DistilBERT model which leverages knowledge
distillation to pretrain a smaller general purpose language model which can be
fine tuned to deliver performances like its larger counterparts. With
DistilBERT sanh et al displayed that it is possible to reach and achieve 97%
of BERTs language understanding capabilities while reducing the size of the
BERT model by 40% while being 60% faster.

Knowledge distillation is a compression technique in which a smaller model i.e
the student model is trained to mimic the behavior of a larger model. A
standard training objective involves reducing the cross entropy loss between
the models predicted distribution and the one-hot factual distribution of the
training labels.

Sanh et al propose three training losses for DistilBERT viz masked language
modeling loss (from the standard MLM training task), cosine embedding loss (to
align the directions of the student and teacher hidden vector states) and
distillation loss. Triple losses make sure that the student model gets trained
properly. DistilBERT in essence has the same architecture as BERT. The token-
type embeddings and pooler are removed while the number of layers is reduced
by a factor of 2. DistilBERT follows the footsteps of Roberta and avoids Next
Sentence Prediction task and is trained on a very large batch size (upto 4K
examples per batch) using dynamic masking.

###  V-D Albert

While RoBERTa focused on enhancing performance of BERT and DistilBERT focused
on efficiency and speed, ALBERT (A Lite BERT) is built to address both. ALBERT
achieves SOTA performance with lower memory usage and increased training speed
in comparison to BERT. Since BERT is parameter inefficient Lan et al [15] in
their paper address this issue by applying techniques to reduce parameters to
1/10th of the original model without substantial performance loss. The authors
experiment with two methods to reduce the model size: 1. Factorized embedding
parametrization and cross-layer parameter sharing. In addition, they improve
the model training by modifying the NSP objective with sentence order
prediction.

Factorized Embedding Parameterization:  In BERT and BERT based models, the
size of the BertWordPiece embeddings and the size of the hidden layers is
linked. Lan et al argue that this is inefficient, because the two have
different purposes. WordPiece Embeddings supposedly capture the general
meaning of the words which is context independent, whereas hidden layers map
the meaning dependent on the specific context. Hidden layers therefore are
much more intricate: they need to store more information and be updated more
often during the training process. In ALBERT, Lan et al. decompose the large
vocabulary matrix into two smaller matrices. Thereby, they separate the size
of the layers and reduce the number of parameters.

Cross-Layer Parameter Sharing:  Another way to reduce parameters is to enable
sharing of parameters across layers. Lan et al try three different settings
for parameter sharing: sharing the feed forward network parameters, the
attention parameters and sharing both at the same time. Parameter sharing
prevents the number of parameters to grow with the depth of a network.

##  VI Results

Metric | Accuracy | Precision | Recall | F1 Score |  | AUC
---
BERT | 0.9691 | 0.7629 | 0.7271 | 0.7446 | 0.9796
DISTILBERT | 0.9663 | 0.7481 | 0.6854 | 0.7154 | 0.9758
ROBERTA | 0.9551 | 0.6869 | 0.5030 | 0.5807 | 0.9484
ALBERT | 0.9495 | 0.6583 | 0.3827 | 0.4840 | 0.9239

Because of a severe imbalance between the class labels with the questions
pertaining to sincere labels being of a higher percentage than Insincere
questions, traditional metrics of validating the model do not capture the
essence of the model in its entirety. Hence we use metrics such as the AUC
score along with F1 score to capture the holistic efficiency of the trained
models.

AUC score : AUC score is a performance measure of classification models at
various threshold levels. It tells how much the model is capable of
distinguishing classes. It plots the false positive rate against the true
positive rate of the model. An ideal model would have an AUC score of 1.

F1 Score : F1 score of a classification model is defined as the harmonic mean
of the precision and recall. Precision is the number of correctly identified
true labels divided by all true labels in the data. Recall is the number of
correctly identified true labels divided by number of true samples that should
have been identified as positive. Higher the F1 score better the performance
of the model.

##  VII Conclusion

In this paper, we aimed to identify Insincere Questions from text state of the
art NLP models. Starting off with simple methods to the most cutting edge, we
illustrated how NLP models can compete with others. In order to do so, we
explored how BERT and three BERT-based transformer models approach text
classification. RoBERTa, DistilBERT, and ALBERT each improve the original
model in a different way with regards to performance and speed. In our
application, we demonstrated the easiest way to implement transformer models,
how to modify the standard settings and what else to pay attention to. On the
task of identifying insincere user intent BERT performed best. However, the
field of NLP is fast moving - and we are excited to see what the next
transformational generation of models will bring.

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