@inproceedings{chandakacherla-etal-2024-uic,
title = "{UIC} {NLP} {GRADS} at {S}em{E}val-2024 Task 3: Two-Step Disjoint Modeling for Emotion-Cause Pair Extraction",
author = "Chandakacherla, Sharad and
Bhargava, Vaibhav and
Parde, Natalie",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.198/",
doi = "10.18653/v1/2024.semeval-1.198",
pages = "1373--1379",
abstract = "Disentangling underlying factors contributing to the expression of emotion in multimodal data is challenging but may accelerate progress toward many real-world applications. In this paper we describe our approach for solving SemEval-2024 Task {\#}3, Sub-Task {\#}1, focused on identifying utterance-level emotions and their causes using the text available from the multimodal F.R.I.E.N.D.S. television series dataset. We propose to disjointly model emotion detection and causal span detection, borrowing a paradigm popular in question answering (QA) to train our model. Through our experiments we find that (a) contextual utterances before and after the target utterance play a crucial role in emotion classification; and (b) once the emotion is established, detecting the causal spans resulting in that emotion using our QA-based technique yields promising results."
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%0 Conference Proceedings
%T UIC NLP GRADS at SemEval-2024 Task 3: Two-Step Disjoint Modeling for Emotion-Cause Pair Extraction
%A Chandakacherla, Sharad
%A Bhargava, Vaibhav
%A Parde, Natalie
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F chandakacherla-etal-2024-uic
%X Disentangling underlying factors contributing to the expression of emotion in multimodal data is challenging but may accelerate progress toward many real-world applications. In this paper we describe our approach for solving SemEval-2024 Task #3, Sub-Task #1, focused on identifying utterance-level emotions and their causes using the text available from the multimodal F.R.I.E.N.D.S. television series dataset. We propose to disjointly model emotion detection and causal span detection, borrowing a paradigm popular in question answering (QA) to train our model. Through our experiments we find that (a) contextual utterances before and after the target utterance play a crucial role in emotion classification; and (b) once the emotion is established, detecting the causal spans resulting in that emotion using our QA-based technique yields promising results.
%R 10.18653/v1/2024.semeval-1.198
%U https://aclanthology.org/2024.semeval-1.198/
%U https://doi.org/10.18653/v1/2024.semeval-1.198
%P 1373-1379
Markdown (Informal)
[UIC NLP GRADS at SemEval-2024 Task 3: Two-Step Disjoint Modeling for Emotion-Cause Pair Extraction](https://aclanthology.org/2024.semeval-1.198/) (Chandakacherla et al., SemEval 2024)
ACL