arXiv subjects

Pushpak Bhattacharyya

Publications and source records attributed to Pushpak Bhattacharyya.

At least 127 records · Page 7Linked to original sources

arXiv2021

COVIDRead: A Large-scale Question Answering Dataset on COVID-19

During this pandemic situation, extracting any relevant information related to COVID-19 will be immensely beneficial to the community at large. In this paper, we present a very important resource, COVIDRead, a Stanford Question Answering Dataset (SQuAD) like dataset over more than 100k question-answer pairs. The dataset consists of Context-Answer-Question triples. Primarily the questions from the context are constructed in an automated way. After that, the system-generated questions are manually checked by hu-mans annotators. This is a precious resource that could serve many purposes, ranging from common people queries regarding this very uncommon disease to managing articles by editors/associate editors of a journal. We establish several end-to-end neural network based baseline models that attain the lowest F1 of 32.03% and the highest F1 of 37.19%. To the best of our knowledge, we are the first to provide this kind of QA dataset in such a large volume on COVID-19. This dataset creates a new avenue of carrying out research on COVID-19 by providing a benchmark dataset and a baseline model.

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arXiv2021

Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages

We explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning. We hypothesize and validate that multilingual fine-tuning of pre-trained language models can yield better performance on downstream NLP applications, compared to models fine-tuned on individual languages. A first of its kind detailed study is presented to track performance change as languages are added to a base language in a graded and greedy (in the sense of best boost of performance) manner; which reveals that careful selection of subset of related languages can significantly improve performance than utilizing all related languages. The Indo-Aryan (IA) language family is chosen for the study, the exact languages being Bengali, Gujarati, Hindi, Marathi, Oriya, Punjabi and Urdu. The script barrier is crossed by simple rule-based transliteration of the text of all languages to Devanagari. Experiments are performed on mBERT, IndicBERT, MuRIL and two RoBERTa-based LMs, the last two being pre-trained by us. Low resource languages, such as Oriya and Punjabi, are found to be the largest beneficiaries of multilingual fine-tuning. Textual Entailment, Entity Classification, Section Title Prediction, tasks of IndicGLUE and POS tagging form our test bed. Compared to monolingual fine tuning we get relative performance improvement of up to 150% in the downstream tasks. The surprise take-away is that for any language there is a particular combination of other languages which yields the best performance, and any additional language is in fact detrimental.

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arXiv2021

M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations

Humor recognition in conversations is a challenging task that has recently gained popularity due to its importance in dialogue understanding, including in multimodal settings (i.e., text, acoustics, and visual). The few existing datasets for humor are mostly in English. However, due to the tremendous growth in multilingual content, there is a great demand to build models and systems that support multilingual information access. To this end, we propose a dataset for Multimodal Multiparty Hindi Humor (M2H2) recognition in conversations containing 6,191 utterances from 13 episodes of a very popular TV series "Shrimaan Shrimati Phir Se". Each utterance is annotated with humor/non-humor labels and encompasses acoustic, visual, and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for humor recognition in conversations. The empirical results on M2H2 dataset demonstrate that multimodal information complements unimodal information for humor recognition. The dataset and the baselines are available at http://www.iitp.ac.in/~ai-nlp-ml/resources.html and https://github.com/declare-lab/M2H2-dataset.

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arXiv2021

Scrambled Translation Problem: A Problem of Denoising UNMT

In this paper, we identify an interesting kind of error in the output of Unsupervised Neural Machine Translation (UNMT) systems like \textit{Undreamt}(footnote). We refer to this error type as \textit{Scrambled Translation problem}. We observe that UNMT models which use \textit{word shuffle} noise (as in case of Undreamt) can generate correct words, but fail to stitch them together to form phrases. As a result, words of the translated sentence look \textit{scrambled}, resulting in decreased BLEU. We hypothesise that the reason behind \textit{scrambled translation problem} is 'shuffling noise' which is introduced in every input sentence as a denoising strategy. To test our hypothesis, we experiment by retraining UNMT models with a simple \textit{retraining} strategy. We stop the training of the Denoising UNMT model after a pre-decided number of iterations and resume the training for the remaining iterations -- which number is also pre-decided -- using original sentence as input without adding any noise. Our proposed solution achieves significant performance improvement UNMT models that train conventionally. We demonstrate these performance gains on four language pairs, \textit{viz.}, English-French, English-German, English-Spanish, Hindi-Punjabi. Our qualitative and quantitative analysis shows that the retraining strategy helps achieve better alignment as observed by attention heatmap and better phrasal translation, leading to statistically significant improvement in BLEU scores.

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arXiv2021

Crosslingual Embeddings are Essential in UNMT for Distant Languages: An English to IndoAryan Case Study

Recent advances in Unsupervised Neural Machine Translation (UNMT) have minimized the gap between supervised and unsupervised machine translation performance for closely related language pairs. However, the situation is very different for distant language pairs. Lack of lexical overlap and low syntactic similarities such as between English and Indo-Aryan languages leads to poor translation quality in existing UNMT systems. In this paper, we show that initializing the embedding layer of UNMT models with cross-lingual embeddings shows significant improvements in BLEU score over existing approaches with embeddings randomly initialized. Further, static embeddings (freezing the embedding layer weights) lead to better gains compared to updating the embedding layer weights during training (non-static). We experimented using Masked Sequence to Sequence (MASS) and Denoising Autoencoder (DAE) UNMT approaches for three distant language pairs. The proposed cross-lingual embedding initialization yields BLEU score improvement of as much as ten times over the baseline for English-Hindi, English-Bengali, and English-Gujarati. Our analysis shows the importance of cross-lingual embedding, comparisons between approaches, and the scope of improvements in these systems.

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arXiv2021

Semantic Extractor-Paraphraser based Abstractive Summarization

The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models. In this manuscript, we propose an extractor-paraphraser based abstractive summarization system that exploits semantic overlap as opposed to its predecessors that focus more on syntactic information overlap. Our model outperforms the state-of-the-art baselines in terms of ROUGE, METEOR and word mover similarity (WMS), establishing the superiority of the proposed system via extensive ablation experiments. We have also challenged the summarization capabilities of the state of the art Pointer Generator Network (PGN), and through thorough experimentation, shown that PGN is more of a paraphraser, contrary to the prevailing notion of a summarizer; illustrating it's incapability to accumulate information across multiple sentences.

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arXiv2021

Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text

We propose a knowledge-based approach for extraction of Cause-Effect (CE) relations from biomedical text. Our approach is a combination of an unsupervised machine learning technique to discover causal triggers and a set of high-precision linguistic rules to identify cause/effect arguments of these causal triggers. We evaluate our approach using a corpus of 58,761 Leukaemia-related PubMed abstracts consisting of 568,528 sentences. We could extract 152,655 CE triplets from this corpus where each triplet consists of a cause phrase, an effect phrase and a causal trigger. As compared to the existing knowledge base - SemMedDB (Kilicoglu et al., 2012), the number of extractions are almost twice. Moreover, the proposed approach outperformed the existing technique SemRep (Rindflesch and Fiszman, 2003) on a dataset of 500 sentences.

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arXiv2021

Techniques for Jointly Extracting Entities and Relations: A Survey

Relation Extraction is an important task in Information Extraction which deals with identifying semantic relations between entity mentions. Traditionally, relation extraction is carried out after entity extraction in a "pipeline" fashion, so that relation extraction only focuses on determining whether any semantic relation exists between a pair of extracted entity mentions. This leads to propagation of errors from entity extraction stage to relation extraction stage. Also, entity extraction is carried out without any knowledge about the relations. Hence, it was observed that jointly performing entity and relation extraction is beneficial for both the tasks. In this paper, we survey various techniques for jointly extracting entities and relations. We categorize techniques based on the approach they adopt for joint extraction, i.e. whether they employ joint inference or joint modelling or both. We further describe some representative techniques for joint inference and joint modelling. We also describe two standard datasets, evaluation techniques and performance of the joint extraction approaches on these datasets. We present a brief analysis of application of a general domain joint extraction approach to a Biomedical dataset. This survey is useful for researchers as well as practitioners in the field of Information Extraction, by covering a broad landscape of joint extraction techniques.

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arXiv2021

Cognitively Aided Zero-Shot Automatic Essay Grading

Automatic essay grading (AEG) is a process in which machines assign a grade to an essay written in response to a topic, called the prompt. Zero-shot AEG is when we train a system to grade essays written to a new prompt which was not present in our training data. In this paper, we describe a solution to the problem of zero-shot automatic essay grading, using cognitive information, in the form of gaze behaviour. Our experiments show that using gaze behaviour helps in improving the performance of AEG systems, especially when we provide a new essay written in response to a new prompt for scoring, by an average of almost 5 percentage points of QWK.

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arXiv2021

Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour

The gaze behaviour of a reader is helpful in solving several NLP tasks such as automatic essay grading. However, collecting gaze behaviour from readers is costly in terms of time and money. In this paper, we propose a way to improve automatic essay grading using gaze behaviour, which is learnt at run time using a multi-task learning framework. To demonstrate the efficacy of this multi-task learning based approach to automatic essay grading, we collect gaze behaviour for 48 essays across 4 essay sets, and learn gaze behaviour for the rest of the essays, numbering over 7000 essays. Using the learnt gaze behaviour, we can achieve a statistically significant improvement in performance over the state-of-the-art system for the essay sets where we have gaze data. We also achieve a statistically significant improvement for 4 other essay sets, numbering about 6000 essays, where we have no gaze behaviour data available. Our approach establishes that learning gaze behaviour improves automatic essay grading.

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arXiv2021

Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays

Most research in the area of automatic essay grading (AEG) is geared towards scoring the essay holistically while there has also been some work done on scoring individual essay traits. In this paper, we describe a way to score essays holistically using a multi-task learning (MTL) approach, where scoring the essay holistically is the primary task, and scoring the essay traits is the auxiliary task. We compare our results with a single-task learning (STL) approach, using both LSTMs and BiLSTMs. We also compare our results of the auxiliary task with such tasks done in other AEG systems. To find out which traits work best for different types of essays, we conduct ablation tests for each of the essay traits. We also report the runtime and number of training parameters for each system. We find that MTL-based BiLSTM system gives the best results for scoring the essay holistically, as well as performing well on scoring the essay traits.

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arXiv2021

Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi

Recently the NLP community has started showing interest towards the challenging task of Hostile Post Detection. This paper present our system for Shared Task at Constraint2021 on "Hostile Post Detection in Hindi". The data for this shared task is provided in Hindi Devanagari script which was collected from Twitter and Facebook. It is a multi-label multi-class classification problem where each data instance is annotated into one or more of the five classes: fake, hate, offensive, defamation, and non-hostile. We propose a two level architecture which is made up of BERT based classifiers and statistical classifiers to solve this problem. Our team 'Albatross', scored 0.9709 Coarse grained hostility F1 score measure on Hostile Post Detection in Hindi subtask and secured 2nd rank out of 45 teams for the task. Our submission is ranked 2nd and 3rd out of a total of 156 submissions with Coarse grained hostility F1 score of 0.9709 and 0.9703 respectively. Our fine grained scores are also very encouraging and can be improved with further finetuning. The code is publicly available.

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arXiv2021

Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy

In this paper, we propose a hybrid technique for semantic question matching. It uses our proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning (DL) or taxonomy-based knowledge alone.

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arXiv2020

EmotionGIF-IITP-AINLPML: Ensemble-based Automated Deep Neural System for predicting category(ies) of a GIF response

In this paper, we describe the systems submitted by our IITP-AINLPML team in the shared task of SocialNLP 2020, EmotionGIF 2020, on predicting the category(ies) of a GIF response for a given unlabelled tweet. For the round 1 phase of the task, we propose an attention-based Bi-directional GRU network trained on both the tweet (text) and their replies (text wherever available) and the given category(ies) for its GIF response. In the round 2 phase, we build several deep neural-based classifiers for the task and report the final predictions through a majority voting based ensemble technique. Our proposed models attain the best Mean Recall (MR) scores of 52.92% and 53.80% in round 1 and round 2, respectively.

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arXiv2020

Reinforced Multi-task Approach for Multi-hop Question Generation

Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer. While sequence to sequence neural models surpass rule-based systems for QG, they are limited in their capacity to focus on more than one supporting fact. For QG, we often require multiple supporting facts to generate high-quality questions. Inspired by recent works on multi-hop reasoning in QA, we take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context. We employ multitask learning with the auxiliary task of answer-aware supporting fact prediction to guide the question generator. In addition, we also proposed a question-aware reward function in a Reinforcement Learning (RL) framework to maximize the utilization of the supporting facts. We demonstrate the effectiveness of our approach through experiments on the multi-hop question answering dataset, HotPotQA. Empirical evaluation shows our model to outperform the single-hop neural question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE, and human evaluation metrics for quality and coverage of the generated questions.

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arXiv2020

Assessing the Severity of Health States based on Social Media Posts

The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request health-related information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert intervention. Therefore, a system capable of assessing the severity of health state from the patients' social media posts can help health professionals (HP) in prioritizing the user's post. In this study, we inspect the efficacy of different aspects of Natural Language Understanding (NLU) to identify the severity of the user's health state in relation to two perspectives(tasks) (a) Medical Condition (i.e., Recover, Exist, Deteriorate, Other) and (b) Medication (i.e., Effective, Ineffective, Serious Adverse Effect, Other) in online health communities. We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. Specifically, our model utilizes the NLU views such as sentiment, emotions, personality, and use of figurative language to extract the contextual information. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.

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arXiv2020

Tweet to News Conversion: An Investigation into Unsupervised Controllable Text Generation

Text generator systems have become extremely popular with the advent of recent deep learning models such as encoder-decoder. Controlling the information and style of the generated output without supervision is an important and challenging Natural Language Processing (NLP) task. In this paper, we define the task of constructing a coherent paragraph from a set of disaster domain tweets, without any parallel data. We tackle the problem by building two systems in pipeline. The first system focuses on unsupervised style transfer and converts the individual tweets into news sentences. The second system stitches together the outputs from the first system to form a coherent news paragraph. We also propose a novel training mechanism, by splitting the sentences into propositions and training the second system to merge the sentences. We create a validation and test set consisting of tweet-sets and their equivalent news paragraphs to perform empirical evaluation. In a completely unsupervised setting, our model was able to achieve a BLEU score of 19.32, while successfully transferring styles and joining tweets to form a meaningful news paragraph.

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arXiv2020

Extracting N-ary Cross-sentence Relations using Constrained Subsequence Kernel

Most of the past work in relation extraction deals with relations occurring within a sentence and having only two entity arguments. We propose a new formulation of the relation extraction task where the relations are more general than intra-sentence relations in the sense that they may span multiple sentences and may have more than two arguments. Moreover, the relations are more specific than corpus-level relations in the sense that their scope is limited only within a document and not valid globally throughout the corpus. We propose a novel sequence representation to characterize instances of such relations. We then explore various classifiers whose features are derived from this sequence representation. For SVM classifier, we design a Constrained Subsequence Kernel which is a variant of Generalized Subsequence Kernel. We evaluate our approach on three datasets across two domains: biomedical and general domain.

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