pwc-archive/papers-with-abstracts huggingface dataset:
Encoder Decoder Approach to Automated Essay Scoring For Deeper Semantic Analysis
Authors: Priyatam Naravajhula, Sreedeep Rayavarapu, Srujana Inturi
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Automated Essay Scoring, Decoder, Descriptive

Descriptive or essay type of answers have always played a major role in education. They clearly capture the student’s grasp on knowledge and presentation skills. Manual essay scoring can be a daunting process to human evaluators; assessing descriptive answers can present a huge overhead owing to limited numbers of evaluators and an out of proportional number of essays to be graded hence leading to an inefficient or an inaccurate score. There has been a major shift in paradigm from traditional classroom education to online education engendered by COVID-19 pandemic; it seems plausible to infer that future assessment of education shall be online, making the solution of automatic essay scorer not only relevant, but of paramount importance. We explore several neural architecture models for the task of automated essay scoring system. Results and Experimental analysis exhibit that our model based on recurrent encoder-decoder provides for a deeper semantic analysis hence, outperforming a strong baseline in terms of quadratic weighted kappa score.
Temporal Question Generation from History Text
Authors: Harsimran Bedi, Sangameshwar Patil, Girish Palshikar
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Question Generation, Question-Generation

Temporal analysis of history text has always held special significance to students, historians and the Social Sciences community in general. We observe from experimental data that existing deep learning (DL) models of ProphetNet and UniLM for question generation (QG) task do not perform satisfactorily when used directly for temporal QG from history text. We propose linguistically motivated templates for generating temporal questions that probe different aspects of history text and show that finetuning the DL models using the temporal questions significantly improves their performance on temporal QG task. Using automated metrics as well as human expert evaluation, we show that performance of the DL models finetuned with the template-based questions is better than finetuning done with temporal questions from SQuAD.
CAWESumm: A Contextual and Anonymous Walk Embedding Based Extractive Summarization of Legal Bills
Authors: Deepali Jain, Malaya Dutta Borah, Anupam Biswas
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Document Embedding, Extractive Summarization, Sentence, Sentence Embedding, Sentence-Embedding

Extractive summarization of lengthy legal documents requires an appropriate sentence scoring mechanism. This mechanism should capture both the local semantics of a sentence as well as the global document-level context of a sentence. The search for an appropriate sentence embedding that can enable an effective scoring mechanism has been the focus of several research works in this domain. In this work, we propose an improved sentence embedding approach that combines a Legal Bert-based local embedding of the sentence with an anonymous random walk-based entire document embedding. Such combined features help effectively capture the local and global information present in a sentence. The experimental results suggest that the proposed sentence embedding approach can be very beneficial for the appropriate representation of sentences in legal documents, improving the sentence scoring mechanism required for extractive summarization of these documents.
Multi-document Text Summarization using Semantic Word and Sentence Similarity: A Combined Approach
Authors: Rajendra Roul
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Sentence, Sentence Similarity, Text Summarization

The exponential growth in the number of text documents produced daily on the web poses several difficulties to people who are responsible for collecting, organizing, and searching different textual content related to a particular topic. Automatic Text Summarization works well in this direction, which can review many documents and pull out the relevant information. But the limitations associated with automatic text summarization need to be removed by finding efficient workarounds. Although current research works have focused on this direction for further improvements, they still face many challenges. This paper proposes a combined semantic-based word and sentence similarity approach to summarize a corpus of text documents. To arrange the sentences in the final summary, KL-divergence technique is used. The experimental work is conducted using DUC datasets, and the obtained results are promising.
#covid is war and #vaccine is weapon? COVID-19 metaphors in India
Authors: Mohammed Khaliq, Rohan Joseph, Sunny Rai
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12

Metaphors are creative cognitive constructs that are employed in everyday conversation to describe abstract concepts and feelings. Prevalent conceptual metaphors such as WAR, MONSTER, and DARKNESS in COVID-19 online discourse sparked a multi-faceted debate over their efficacy in communication, resultant psychological impact on listeners, and their appropriateness in social discourse. In this work, we investigate metaphors used in discussions around COVID-19 on Indian Twitter. We observe subtle transitions in metaphorical mappings as the pandemic progressed. Our experiments, however, didn’t indicate any affective impact of WAR metaphors on the COVID-19 discourse.
Studies Towards Language Independent Fake News Detection
Authors: Soumayan Majumder, Dipankar Das
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Fake News Detection

We have studied that fake news is currently one of the trending topic and it causes problem to many people and organization. We use COVID19 domain and 7 languages to work on. We collect our data from twitter. We build two types of model one is language dependent and other one is language independent. We get better result in language independent model for English, Hindi and Bengali language. Results of European languages like German, Italian, French and Spanish are comparable in both language dependent and independent model.
Wikipedia Current Events Summarization using Particle Swarm Optimization
Authors: Santosh Kumar Mishra, Darsh Kaushik, Sriparna Saha, Pushpak Bhattacharyya
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Clustering

This paper proposes a method to summarize news events from multiple sources. We pose event summarization as a clustering-based optimization problem and solve it using particle swarm optimization. The proposed methodology uses the search capability of particle swarm optimization, detecting the number of clusters automatically. Experiments are conducted with the Wikipedia Current Events Portal dataset and evaluated using the well-known ROUGE-1, ROUGE-2, and ROUGE-L scores. The obtained results show the efficacy of the proposed methodology over the state-of-the-art methods. It attained improvement of 33.42%, 81.75%, and 57.58% in terms of ROUGE-1, ROUGE-2, and ROUGE-L, respectively.
Prediction of Video Game Development Problems Based on Postmortems using Different Word Embedding Techniques
Authors: Anirudh A, Aman RAJ Singh, Anjali Goyal, Lov Kumar, N L Bhanu Murthy
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Marketing

The interactive entertainment industry is being actively involved with the development, marketing and sale of video games in the past decade. The increasing interest in video games has led to an increase in video game development techniques and methods. It has emerged as an immensely large sector, and now it has grown to be larger than the movie and music industry combined. The postmortem of a game outlines and analyzes the game’s history, team goals, what went right, and what went wrong with the game. Despite its significance, there is little understanding related to the challenges encountered by the programmers. Postmortems are not properly maintained and are informally written, leading to a lack of trustworthiness. In this study, we perform a systematic analysis on different problems faced in the video game development. The need for automation and ML techniques arises because it could help game developers easily identify the exact problem from the description, and hence be able to easily find a solution. This work could also help developers in identifying frequent mistakes that could be avoided, and will provide researchers a beginning point to further consider game development in context of software engineering.
Multi-task pre-finetuning for zero-shot cross lingual transfer
Authors: Moukthika Yerramilli, Pritam Varma, Anurag Dwarakanath
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Cross-Lingual Transfer, intent-classification, Intent Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Zero-Shot Cross-Lingual Transfer

Building machine learning models for low resource languages is extremely challenging due to the lack of available training data (either un-annotated or annotated). To support such scenarios, zero-shot cross lingual transfer is used where the machine learning model is trained on a resource rich language and is directly tested on the resource poor language. In this paper, we present a technique which improves the performance of zero-shot cross lingual transfer. Our method performs multi-task pre-finetuning on a resource rich language using a multilingual pre-trained model. The pre-finetuned model is then tested in a zero-shot manner on the resource poor languages. We test the performance of our method on 8 languages and for two tasks, namely, Intent Classification (IC) & Named Entity Recognition (NER) using the MultiAtis++ dataset. The results showed that our method improves IC performance in 7 out of 8 languages and NER performance in 4 languages. Our method also leads to faster convergence during finetuning. The usage of pre-finetuning demonstrates a data efficient way for supporting new languages and geographies across the world.
Sentiment Analysis For Bengali Using Transformer Based Models
Authors: Anirban Bhowmick, Abhik Jana
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Sentiment Analysis, Sentiment Classification

Sentiment analysis is one of the key Natural Language Processing (NLP) tasks that has been attempted by researchers extensively for resource-rich languages like English. But for low resource languages like Bengali very few attempts have been made due to various reasons including lack of corpora to train machine learning models or lack of gold standard datasets for evaluation. However, with the emergence of transformer models pre-trained in several languages, researchers are showing interest to investigate the applicability of these models in several NLP tasks, especially for low resource languages. In this paper, we investigate the usefulness of two pre-trained transformers models namely multilingual BERT and XLM-RoBERTa (with fine-tuning) for sentiment analysis for the Bengali Language. We use three datasets for the Bengali language for evaluation and produce state-of-the-art performance, even reaching a maximum of 95% accuracy for a two-class sentiment classification task. We believe, this work can serve as a good benchmark as far as sentiment analysis for the Bengali language is concerned.
IndicFed: A Federated Approach for Sentiment Analysis in Indic Languages
Authors: Jash Mehta, Deep Gandhi, Naitik Rathod, Sudhir Bagul
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Federated Learning, Privacy Preserving, Sentiment Analysis

The task of sentiment analysis has been extensively studied in high-resource languages. Even though sentiment analysis is studied for some resource-constrained languages, the corpora and the datasets available in other low resource languages are scarce and fragmented. This prevents further research of resource-constrained languages and also inhibits model performance for these languages. Privacy concerns may also be raised while aggregating some datasets for training central models. Our work tries to steer the research of sentiment analysis for resource-constrained languages in the direction of Federated Learning. We conduct various experiments to compare server based and federated approaches for 4 Indic Languages - Marathi, Hindi, Bengali, and Telugu. Specifically, we show that a privacy preserving approach, Federated Learning surpasses traditional server trained LSTM model and exhibits comparable performance to other servers-side transformer models.
An Efficient BERT Based Approach to Detect Aggression and Misogyny
Authors: Sandip Dutta, Utso Majumder, Sudip Naskar
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12

Social media is bustling with ever growing cases of trolling, aggression and hate. A huge amount of social media data is generated each day which is insurmountable for manual inspection. In this work, we propose an efficient and fast method to detect aggression and misogyny in social media texts. We use data from the Second Workshop on Trolling, Aggression and Cyber Bullying for our task. We employ a BERT based model to augment our data. Next we employ Tf-Idf and XGBoost for detecting aggression and misogyny. Our model achieves 0.73 and 0.85 Weighted F1 Scores on the 2 prediction tasks, which are comparable to the state of the art. However, the training time, model size and resource requirements of our model are drastically lower compared to the state of the art models, making our model useful for fast inference.
How vulnerable are you? A Novel Computational Psycholinguistic Analysis for Phishing Influence Detection
Authors: Anik Chatterjee, Sagnik Basu
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12

This document contains our work and progress regarding phishing detection by searching for proper influential sentences. Currently, the world is becoming smart, as a result most of the transactions and posting offers happen online. So, human beings have become the most vulnerable to security breach or hacking through phishing attacks, or being persuaded through influential texts in social media sites. We have analyzed influential and non-influential sentences and populated our dataset with those. We have proposed a computational model for implementing Cialdini and we got state of the art accuracy with our model. Our approach is language independent and domain independent and it is applicable to any problem where persuation detection is important. Our dataset and proposed computational psycholinguistic approach will motivate researchers to work more in the area of persuasion detection.
Retrofitting of Pre-trained Emotion Words with VAD-dimensions and the Plutchik Emotions
Authors: Manasi Kulkarni, Pushpak Bhattacharyya
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Emotion Recognition, Word Embeddings

The word representations are based on distributional hypothesis according to which words that occur in the similar contexts, tend to have a similar meaning and appear closer in vector space. For example, the emotionally dissimilar words ”joy” and ”sadness” have higher cosine similarity. The existing pre-trained embedding models lack in emotional words interpretations. For creating our VAD-Emotion embeddings, we modify the pre-trained word embeddings with emotion information. This is a lexicons based approach that uses the Valence, Arousal and Dominance (VAD) values, and the Plutchik’s emotions to incorporate the emotion information in pre-trained word embeddings using post-training processing. This brings emotionally similar words nearer and emotionally dissimilar words away from each other in the proposed vector space. We demonstrate the performance of proposed embedding through NLP downstream task - Emotion Recognition.
Evaluating Pretrained Transformer Models for Entity Linking inTask-Oriented Dialog
Authors: Sai Muralidhar Jayanthi, Varsha Embar, Karthik Raghunathan
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Entity Linking, text similarity

The wide applicability of pretrained transformer models (PTMs) for natural language tasks is well demonstrated, but their ability to comprehend short phrases of text is less explored. To this end, we evaluate different PTMs from the lens of unsupervised Entity Linking in task-oriented dialog across 5 characteristics– syntactic, semantic, short-forms, numeric and phonetic. Our results demonstrate that several of the PTMs produce sub-par results when compared to traditional techniques, albeit competitive to other neural baselines. We find that some of their shortcomings can be addressed by using PTMs fine-tuned for text-similarity tasks, which illustrate an improved ability in comprehending semantic and syntactic correspondences, as well as some improvements for short-forms, numeric and phonetic variations in entity mentions. We perform qualitative analysis to understand nuances in their predictions and discuss scope for further improvements.
eaVQA: An Experimental Analysis on Visual Question Answering Models
Authors: Souvik Chowdhury, Badal Soni
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) has recently become a popular research area. VQA problem lies in the boundary of Computer Vision and Natural Language Processing research domains. In VQA research, the dataset is a very important aspect because of its variety in image types i.e. natural and synthetic and also question answer source i.e. originated from human source or computer-generated question answer. Various details about each dataset is given in this paper, which can help future researchers to a great extent. In this paper, we discussed and compared the experimental performance of Stacked Attention Network Model (SANM) and bidirectional LSTM and MUTAN based fusion models. As per the experimental results, MUTAN accuracy and loss are 29% and 3.5 respectively. SANM model is giving 55% accuracy and a loss of 2.2 whereas VQA model is giving 59% accuracy and 1.9 loss.
Deep Embedding of Conversation Segments
Authors: Abir Chakraborty, Anirban Majumder
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Clustering, Sentence

We introduce a novel conversation embedding by extending Bidirectional Encoder Representations from Transformers (BERT) framework. Specifically, information related to “turn” and “role” that are unique to conversations are augmented to the word tokens and the next sentence prediction task predicts a segment of a conversation possibly spanning across multiple roles and turns. It is observed that the addition of role and turn substantially increases the next sentence prediction accuracy. Conversation embeddings obtained in this fashion are applied to (a) conversation clustering, (b) conversation classification and (c) as a context for automated conversation generation on new datasets (unseen by the pre-training model). We found that clustering accuracy is greatly improved if embeddings are used as features as opposed to conventional tf-idf based features that do not take role or turn information into account. On classification task, a fine-tuned model on conversation embedding achieves accuracy comparable to an optimized linear SVM model on tf-idf based features. Finally, we present a way of capturing variable length context in sequence-to-sequence models by utilizing this conversation embedding and show that BLEU score improves over a vanilla sequence to sequence model without context.
DialogActs based Search and Retrieval for Response Generation in Conversation Systems
Authors: Nidhi Arora, Rashmi Prasad, Srinivas Bangalore
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Response Generation, Retrieval

Designing robust conversation systems with great customer experience requires a team of design experts to think of all probable ways a customer can interact with the system and then author responses for each use case individually. The responses are authored from scratch for each new client and application even though similar responses have been created in the past. This happens largely because the responses are encoded using domain specific set of intents and entities. In this paper, we present preliminary work to define a dialog act schema to merge and map responses from different domains and applications using a consistent domain-independent representation. These representations are stored and maintained using an Elasticsearch system to facilitate generation of responses through a search and retrieval process. We experimented generating different surface realizations for a response given a desired information state of the dialog.
An On-device Deep-Learning Approach for Attribute Extraction from Heterogeneous Unstructured Text
Authors: Mahesh Gorijala, Aniruddha Bala, Pinaki Bhaskar, Krishnaditya ., Vikram Mupparthi
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Attribute, Attribute Extraction

Mobile devices, with their rapidly growing usage, have turned into rich sources of user information, holding critical insights for betterment of user experience and personalization. Creating, receiving and storing important information in the form of unstructured text has become a part and parcel of daily routine of users. From purchase deliveries in Short Message Service (SMS) or Notifications, to event booking details in Calendar applications, mobile devices serve as a portal for understanding user interests, behaviours and activities through information extraction. In this paper, we address the challenge of on-device extraction of user information from unstructured data in natural language from heterogeneous sources like messages, notification, calendar etc. The issue of privacy concern is effectively eliminated by the on-device nature of the proposed solution. Our proposed solution consists of 3 components – A Na ̈ıve-Bayes based classifier for domain identification, a Dual Character andWord based Bidirectional Long Short Term Memory (Bi-LSTM) and Conditional Random Field (CRF) model for attribute extraction and a rule-based Entity Linker. Our solution achieved a 93.29% F1 score on five domains (shopping, travel, event, service and personal). Since on-device deployment has memory and latency constraints, we ensure minimal model size and optimal inference latency. To demonstrate the efficacy of our approach, we have experimented on CoNLL- 2003 dataset and achieved comparable performance to existing benchmark results.
Weakly Supervised Extraction of Tasks from Text
Authors: Sachin Pawar, Girish Palshikar, Anindita Sinha Banerjee
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12

In this paper, we propose a novel problem of automatic extraction of tasks from text. A task is a well-defined knowledge-based volitional action. We describe various characteristics of tasks as well as compare and contrast them with events. We propose two techniques for task extraction – i) using linguistic patterns and ii) using a BERT-based weakly supervised neural model. We evaluate our techniques with other competent baselines on 4 datasets from different domains. Overall, the BERT-based weakly supervised neural model generalizes better across multiple domains as compared to the purely linguistic patterns based approach.