no code implementations • 29 Feb 2024 • Shivani Kumar, Md Shad Akhtar, Erik Cambria, Tanmoy Chakraborty
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.
no code implementations • 29 Feb 2024 • Prottay Kumar Adhikary, Aseem Srivastava, Shivani Kumar, Salam Michael Singh, Puneet Manuja, Jini K Gopinath, Vijay Krishnan, Swati Kedia, Koushik Sinha Deb, Tanmoy Chakraborty
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.
no code implementations • 18 Jan 2024 • Shivani Kumar, Tanmoy Chakraborty
In this study, we explore response generation within code-mixed conversations.
1 code implementation • 19 Oct 2023 • Shivani Kumar, Ramaneswaran S, Md Shad Akhtar, Tanmoy Chakraborty
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.
no code implementations • 14 Jul 2023 • Shivani Kumar, Sumit Bhatia, Milan Aggarwal, Tanmoy Chakraborty
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.
no code implementations • 24 Jun 2023 • Shivani Kumar, Shubham Dudeja, Md Shad Akhtar, Tanmoy Chakraborty
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.
no code implementations • 18 Apr 2023 • Shivani Kumar, Rishabh Gupta, Md Shad Akhtar, Tanmoy Chakraborty
We have evaluated various baselines on this dataset and benchmarked it with a new neural model, SPOT, which we introduce in this paper.
1 code implementation • 20 Nov 2022 • Shivani Kumar, Ishani Mondal, Md Shad Akhtar, Tanmoy Chakraborty
To this end, we explore the task of Sarcasm Explanation in Dialogues, which aims to unfold the hidden irony behind sarcastic utterances.
1 code implementation • ACL 2022 • Shivani Kumar, Atharva Kulkarni, Md Shad Akhtar, Tanmoy Chakraborty
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
1 code implementation • 20 May 2021 • Manjot Bedi, Shivani Kumar, Md Shad Akhtar, Tanmoy Chakraborty
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.
no code implementations • 23 Mar 2021 • Shivani Kumar, Anubhav Shrimal, Md Shad Akhtar, Tanmoy Chakraborty
Therefore, discovering the reasons (triggers) behind the speaker's emotion-flip during a conversation is essential to explain the emotion labels of individual utterances.