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Found 36 papers, 21 papers with code
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.
Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation
TURBO assumes the target of the sarcasm and guides the multimodal shared fusion mechanism in learning intricacies of the intended irony for explanations.
Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification
Next, we propose a commonsense and domain-enriched framework, M3H, to enhance MLMs' ability to interpret figurative language and commonsense knowledge.
Trust Modeling in Counseling Conversations: A Benchmark Study
The therapeutic bond between a patient and a therapist directly correlates with effective mental health counseling.
Knowledge Planning in Large Language Models for Domain-Aligned Counseling Summarization
We further benchmark PIECE with other LLMs and report improvement, including Llama-2 (+2. 72%), Mistral (+2. 04%), and Zephyr (+1. 59%), to justify the generalizability of the planning engine.
Crowd Intelligence for Early Misinformation Prediction on Social Media
We introduce CROWDSHIELD, a crowd intelligence-based method for early misinformation prediction.
Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech
Employing language models to generate explanations for an incoming implicit hate post is an active area of research.
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.
Intent-conditioned and Non-toxic Counterspeech Generation using Multi-Task Instruction Tuning with RLAIF
Counterspeech, defined as a response to mitigate online hate speech, is increasingly used as a non-censorial solution.
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.
EROS: Entity-Driven Controlled Policy Document Summarization
In this paper, we propose to enhance the interpretability and readability of policy documents by using controlled abstractive summarization -- we enforce the generated summaries to include critical privacy-related entities (e. g., data and medium) and organization's rationale (e. g., target and reason) in collecting those entities.
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.
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 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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
Hostility Detection Dataset in Hindi
In this paper, we present a novel hostility detection dataset in Hindi language.
Fighting an Infodemic: COVID-19 Fake News Dataset
This is further exacerbated at the time of a pandemic.
