Search Results for author:
Found 123 papers, 61 papers with code
Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes
We present the findings of the shared task at the CONSTRAINT 2022 Workshop: Hero, Villain, and Victim: Dissecting harmful memes for Semantic role labeling of entities.
Document Retrieval and Claim Verification to Mitigate COVID-19 Misinformation
The current vogue is to employ manual fact-checkers to verify claims to combat this avalanche of misinformation.
$\texttt{LM}^\texttt{2}$: A Simple Society of Language Models Solves Complex Reasoning
The solver model generates the solution to the subproblems that are then checked by the verifier module; depending upon the feedback from the verifier, the reasoning context is constructed using the subproblems and the solutions.
Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation
In this paper, we tackle the problem of code-mixed (Hinglish and Bengalish) to English machine translation.
SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)
We present SemEval-2024 Task 10, a shared task centred on identifying emotions and finding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues.
Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Study
Further, expert evaluation reveals that Mistral supersedes both MentalLlama and MentalBART based on six parameters -- affective attitude, burden, ethicality, coherence, opportunity costs, and perceived effectiveness.
How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning
Despite superior reasoning prowess demonstrated by Large Language Models (LLMs) with Chain-of-Thought (CoT) prompting, a lack of understanding prevails around the internal mechanisms of the models that facilitate CoT generation.
FLAME: Self-Supervised Low-Resource Taxonomy Expansion using Large Language Models
In this paper, we propose FLAME, a novel approach for taxonomy expansion in low-resource environments by harnessing the capabilities of large language models that are trained on extensive real-world knowledge.
Probing Critical Learning Dynamics of PLMs for Hate Speech Detection
Despite the widespread adoption, there is a lack of research into how various critical aspects of pretrained language models (PLMs) affect their performance in hate speech detection.
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models
In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content.
Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues
In this study, we explore response generation within code-mixed conversations.
Exploiting Representation Bias for Data Distillation in Abstractive Text Summarization
We employ clustering techniques to learn the diversity of a model's sample space and how data points are mapped from the embedding space to the encoder space and vice versa.
Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning
In this paper, we start with the hypothesis that much smaller LMs, which are weak at multi-step reasoning, can achieve reasonable arithmetic reasoning if arithmetic word problems are posed as a formalize-then-solve task.
Ranked #11 on
Math Word Problem Solving
on SVAMP
(using extra training data)
Uncertainty in Additive Feature Attribution methods
As a result, we propose a measure to quantify the relative complexity of a blackbox classifier.
Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span Detection
As hate speech continues to proliferate on the web, it is becoming increasingly important to develop computational methods to mitigate it.
Overview of the CLAIMSCAN-2023: Uncovering Truth in Social Media through Claim Detection and Identification of Claim Spans
However, the sheer volume of information makes it difficult to identify fake news effectively.
Manifold-Preserving Transformers are Effective for Short-Long Range Encoding
In this work, we propose TransJect, an encoder model that guarantees a theoretical bound for layer-wise distance preservation between a pair of tokens.
From Chaos to Clarity: Claim Normalization to Empower Fact-Checking
To evaluate the effectiveness of our proposed model, we meticulously compile a comprehensive real-world dataset, CLAN, comprising more than 6k instances of social media posts alongside their respective normalized claims.
Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning
Additionally, we show that DaSLaM is not limited by the solver's capabilities as a function of scale; e. g., solver LMs with diverse sizes give significant performance improvement with our solver-agnostic decomposition technique.
Ranked #6 on
Overall - Test
on JEEBench
(using extra training data)
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues
Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions.
Factuality Challenges in the Era of Large Language Models
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention.
Focal Inferential Infusion Coupled with Tractable Density Discrimination for Implicit Hate Speech Detection
Although pre-trained large language models (PLMs) have achieved state-of-the-art on many NLP tasks, they lack understanding of subtle expressions of implicit hate speech.
Leveraging Social Discourse to Measure Check-worthiness of Claims for Fact-checking
Currently, these claims are manually verified by fact-checkers.
Persona-aware Generative Model for Code-mixed Language
We propose a Persona-aware Generative Model for Code-mixed Generation, PARADOX, a novel Transformer-based encoder-decoder model that encodes an utterance conditioned on a user's persona and generates code-mixed texts without monolingual reference data.
Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communities
Online Mental Health Communities (OMHCs), such as Reddit, have witnessed a surge in popularity as go-to platforms for seeking information and support in managing mental health needs.
Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems
To this end, we propose UNIT, a UNified dIalogue dataseT constructed from conversations of existing datasets for different dialogue tasks capturing the nuances for each of them.
Fusing Multimodal Signals on Hyper-complex Space for Extreme Abstractive Text Summarization (TL;DR) of Scientific Contents
In this paper, we deal with a novel task of extreme abstractive text summarization (aka TL;DR generation) by leveraging multiple input modalities.
Emotion Flip Reasoning in Multiparty Conversations
In this paper, we explore the task called Instigator based Emotion Flip Reasoning (EFR), which aims to identify the instigator behind a speaker's emotion flip within a conversation.
Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment
Our proposed solution HEN-mBERT is a modular, multilingual, mixture-of-experts model that enriches the linguistic subspace with latent endogenous signals from history, topology, and exemplars.
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization
In this work, we propose a novel task, MEMEX - given a meme and a related document, the aim is to mine the context that succinctly explains the background of the meme.
Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation
Due to the complex and multifaceted nature of hate speech, utilizing multiple forms of counter-narratives with varying intents may be advantageous in different circumstances.
Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment
In-context learning (ICL) unfolds as large language models become capable of inferring test labels conditioned on a few labeled samples without any gradient update.
Learning and Reasoning Multifaceted and Longitudinal Data for Poverty Estimates and Livelihood Capabilities of Lagged Regions in Rural India
The proposed project aims to examine the poverty situation of rural India for the period of 1990-2022 based on the quality of life and livelihood indicators.
Speaker Profiling in Multiparty Conversations
We have evaluated various baselines on this dataset and benchmarked it with a new neural model, SPOT, which we introduce in this paper.
Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection
However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages.
Inline Citation Classification using Peripheral Context and Time-evolving Augmentation
Current datasets and methods used for inline citation classification only use citation-marked sentences constraining the model to turn a blind eye to domain knowledge and neighboring contextual sentences.
Hatemongers ride on echo chambers to escalate hate speech diffusion
We analyze more than 32 million posts from over 6. 8 million users across three popular online social networks to investigate the interrelations between hateful behavior, information dissemination, and polarised organization mediated by echo chambers.
Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling
Virtual Mental Health Assistants (VMHAs) have become a prevalent method for receiving mental health counseling in the digital healthcare space.
Node Injection for Class-specific Network Poisoning
In this paper, we propose a novel problem statement - a class-specific poison attack on graphs in which the attacker aims to misclassify specific nodes in the target class into a different class using node injection.
Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?
A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities.
What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes
Here, we introduce a novel task - EXCLAIM, generating explanations for visual semantic role labeling in memes.
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues
To this end, we explore the task of Sarcasm Explanation in Dialogues, which aims to unfold the hidden irony behind sarcastic utterances.
Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter
The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation.
Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis
Existing self-supervised learning strategies are constrained to either a limited set of objectives or generic downstream tasks that predominantly target uni-modal applications.
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification
The surface-level signals expressed by a social-text itself may not be adequate for such tasks; therefore, recent methods attempted to incorporate other intrinsic signals such as user behavior and the underlying graph structure.
Predicting Hate Intensity of Twitter Conversation Threads
Tweets are the most concise form of communication in online social media, wherein a single tweet has the potential to make or break the discourse of the conversation.
Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering
A structured counseling conversation may contain discussions about symptoms, history of mental health issues, or the discovery of the patient's behavior.
Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization
Curbing online hate speech has become the need of the hour; however, a blanket ban on such activities is infeasible for several geopolitical and cultural reasons.
DISARM: Detecting the Victims Targeted by Harmful Memes
Finally, we show that DISARM is interpretable and comparatively more generalizable and that it can reduce the relative error rate for harmful target identification by up to 9 points absolute over several strong multimodal rivals.
Detecting and Understanding Harmful Memes: A Survey
One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.
A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer
Learning the semantics and morphology of code-mixed language remains a key challenge, due to scarcity of data and unavailability of robust and language-invariant representation learning technique.
Detecting Anchors' Opinion in Hinghlish News Delivery
A total of 2054 anchors' utterances in the dataset are marked as opinionated or non-opinionated.
Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?
Identifying argument components from unstructured texts and predicting the relationships expressed among them are two primary steps of argument mining.
A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework
We collect a corpus of $150k$ items (posts and comments) annotated using the subreddit labels and transfer learning approaches.
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues
In this work, we study the discourse structure of sarcastic conversations and propose a novel task - Sarcasm Explanation in Dialogue (SED).
Ranked #1 on
Sarcasm Detection
on WITS
Handling Bias in Toxic Speech Detection: A Survey
While reducing toxicity on online platforms continues to be an active area of research, a systematic study of various biases and their mitigation strategies will help the research community produce robust and fair models.
Why Did You Not Compare With That? Identifying Papers for Use as Baselines
We frame the problem as a binary classification task where all the references in a paper are to be classified as either baselines or non-baselines.
Nipping in the Bud: Detection, Diffusion and Mitigation of Hate Speech on Social Media
We discuss a series of our proposed solutions to limit the spread of hate speech on social media.
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision
Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection.
Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanation
In this paper, we propose a novel problem -- Multimodal Sarcasm Explanation (MuSE) -- given a multimodal sarcastic post containing an image and a caption, we aim to generate a natural language explanation to reveal the intended sarcasm.
Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations
We identify the requirement of such conversation and propose twelve domain-specific dialogue-act (DAC) labels.
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems
The Transformer and its variants have been proven to be efficient sequence learners in many different domains.
Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings
Euclidean word embedding models such as GloVe and Word2Vec have been shown to reflect human-like gender biases.
Detecting Harmful Memes and Their Targets
In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target.
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets
We focus on two tasks: (i)detecting harmful memes, and (ii)identifying the social entities they target.
Assessing the Quality of the Datasets by Identifying Mislabeled Samples
Therefore, by identifying the outliers in the latent space, we can find the mislabeled samples.
QUINT: Node embedding using network hashing
QUINT is the first of its kind that provides tremendous gain in terms of speed and space usage without compromising much on the accuracy of the downstream tasks.
DESYR: Definition and Syntactic Representation Based Claim Detection on the Web
To demarcate between a claim and a non-claim is arduous for both humans and machines, owing to latent linguistic variance between the two and the inadequacy of extensive definition-based formalization.
GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs
Specifically, the generative model learns to approximate the distribution of anomalous samples from the candidate set of graph snapshots, and the discriminative model detects whether the sampled snapshot is from the ground-truth or not.
Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals
Second, there is a measurable positive correlation between the novelty of the root content (with respect to a streaming external corpus) and the relative size of the resulting cascade.
HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation
In this paper, we propose HIT, a robust representation learning method for code-mixed texts.
Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations
In this work, we make two major contributions considering the above limitations: (1) we develop a Hindi-English code-mixed dataset, MaSaC, for the multi-modal sarcasm detection and humor classification in conversational dialog, which to our knowledge is the first dataset of its kind; (2) we propose MSH-COMICS, a novel attention-rich neural architecture for the utterance classification.
See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarization
However, existing methods use short videos as the visual modality and short summary as the ground-truth, therefore, perform poorly on lengthy videos and long ground-truth summary.
Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets
In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labeled data.
An Embedding-based Joint Sentiment-Topic Model for Short Texts
Many companies need to extract meaningful information (which may include thematic content as well as semantic polarity) out of such short texts to understand users' behaviour.
Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer
Therefore, discovering the reasons (triggers) behind the speaker's emotion-flip during a conversation is essential to explain the emotion labels of individual utterances.
Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis
We evaluate MHA-MEME on the 'Memotion Analysis' dataset for all three sub-tasks - sentiment classification, affect classification, and affect class quantification.
A Survey on Multimodal Disinformation Detection
As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content.
Hide and Seek: Outwitting Community Detection Algorithms
This leads to the problem of community deception which investigates the avenues of minimally rewiring nodes in a network so that a given target community maximally hides from a community detection algorithm.
Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection
As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media.
Adversarial Attack on Network Embeddings via Supervised Network Poisoning
An important consideration in such applications is the robustness of the embedding algorithms against adversarial attacks, which can be examined by performing perturbation on the original network.
TruthBot: An Automated Conversational Tool for Intent Learning, Curated Information Presenting, and Fake News Alerting
Each class is implemented as a separate module that uses either its own curated knowledge-base or searches the web to obtain the correct information.
LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content
We resolve the latter issue by annotating a Twitter dataset which aims at providing a testing ground on a large unstructured dataset.
SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!
The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes.
Fighting an Infodemic: COVID-19 Fake News Dataset
This is further exacerbated at the time of a pandemic.
Hostility Detection Dataset in Hindi
In this paper, we present a novel hostility detection dataset in Hindi language.
Attention Beam: An Image Captioning Approach
The aim of image captioning is to generate textual description of a given image.
Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter
Meanwhile, to predict the retweet dynamics on Twitter, we propose RETINA, a novel neural architecture that incorporates exogenous influence using scaled dot-product attention.
Corpora Evaluation and System Bias Detection in Multi-document Summarization
Owing to no standard definition of the task, we encounter a plethora of datasets with varying levels of overlap and conflict between participating documents.
Dynamics of node influence in network growth models
In this paper, we study how a metric for node influence in network growth models behaves over time as the network evolves.
SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020).
SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!
The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes.
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings
We also propose a new bias evaluation metric - Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections.
Neural Abstractive Summarization with Structural Attention
This problem is exacerbated in multi-document summarization tasks such as summarizing the popular opinion in threads present in community question answering (CQA) websites such as Yahoo!
Abstractive Text Summarization
Community Question Answering
+3
Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future
It is supposed that the present prediction models will assist the government and medical personnel to be prepared for the upcoming conditions and have more readiness in healthcare systems.
Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews
This often is done collectively in groups.
Aspect-based Academic Search using Domain-specific KB
Often, the user is also aware of the "aspect" to retrieve a relevant document.
Unsupervised Sentiment Analysis for Code-mixed Data
In this work, we introduce methods that use different kinds of multilingual and cross-lingual embeddings to efficiently transfer knowledge from monolingual text to code-mixed text for sentiment analysis of code-mixed text.
All It Takes is 20 Questions!: A Knowledge Graph Based Approach
One player is the answerer, and the other is a questioner.
Memeify: A Large-Scale Meme Generation System
The dataset consists of 1. 1 million meme captions from 128 classes.
Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory
Unlike previous studies which model a discussion in a static manner, in the present study, we model it as a time-varying process and solve two inter-related problems -- predict which user groups will get engaged with an ongoing discussion, and forecast the growth rate of a discussion in terms of the number of comments.
Multitask Learning for Blackmarket Tweet Detection
Online social media platforms have made the world more connected than ever before, thereby making it easier for everyone to spread their content across a wide variety of audiences.
DiffQue: Estimating Relative Difficulty of Questions in Community Question Answering Services
In this paper, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem.
Clark Kent at SemEval-2019 Task 4: Stylometric Insights into Hyperpartisan News Detection
In this paper, we present a news bias prediction system, which we developed as part of a SemEval 2019 task.
Spotting Collective Behaviour of Online Frauds in Customer Reviews
Online reviews play a crucial role in deciding the quality before purchasing any product.
Fiducia: A Personalized Food Recommender System for Zomato
This paper presents Fiducia, a food review system involving a pipeline which processes restaurant-related reviews obtained from Zomato (India's largest restaurant search and discovery service).
Multi-task Learning for Target-dependent Sentiment Classification
The auxiliary and main GRUs send their states to a different fully connected layer, trained for the main task.
Heterogeneous Edge Embeddings for Friend Recommendation
We propose a friend recommendation system (an application of link prediction) using edge embeddings on social networks.
GIRNet: Interleaved Multi-Task Recurrent State Sequence Models
A primary instance is also submitted to each auxiliary RNN, but their state sequences are gated and merged into a novel composite state sequence tailored to the primary inference task.
How did the discussion go: Discourse act classification in social media conversations
Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions.
Retweet Us, We Will Retweet You: Spotting Collusive Retweeters Involved in Blackmarket Services
Thus they become customers of blackmarket syndicates and engage in fake activities.
Social and Information Networks
DyPerm: Maximizing Permanence for Dynamic Community Detection
In this paper, we propose DyPerm, the first dynamic community detection method which optimizes a novel community scoring metric, called permanence.
Social and Information Networks Physics and Society
EC3: Combining Clustering and Classification for Ensemble Learning
In this paper, We propose a novel algorithm, called EC3 that merges classification and clustering together in order to support both binary and multi-class classification.
MMGAN: Manifold Matching Generative Adversarial Network
MMGAN finds two manifolds representing the vector representations of real and fake images.
Determining sentiment in citation text and analyzing its impact on the proposed ranking index
Analysis of these sentiments is one of the popular research areas of present day researchers.
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
Online content publishers often use catchy headlines for their articles in order to attract users to their websites.
All Fingers are not Equal: Intensity of References in Scientific Articles
Research accomplishment is usually measured by considering all citations with equal importance, thus ignoring the wide variety of purposes an article is being cited for.
An effective AHP-based metaheuristic approach to solve supplier selection problem
The supplier selection problem is based on electing the best supplier from a group of pre-specified candidates, is identified as a Multi Criteria Decision Making (MCDM), is proportionately significant in terms of qualitative and quantitative attributes.
Identifying Bengali Multiword Expressions using Semantic Clustering
As a by-product of this experiment, we have started developing a standard lexicon in Bengali that serves as a productive Bengali linguistic thesaurus.
