Search Results for author:
Found 11 papers, 4 papers with code
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.
Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues
In this study, we explore response generation within code-mixed conversations.
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.
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.
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.
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
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.
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.
