SubmissionNumber#=%=#4
FinalPaperTitle#=%=#nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion Cause in Conversations with Chain-of-Thought on Emotion States
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Nicolay Rusnachenko
JobTitle#==#Research Fellow in MMI-NLP
Organization#==#Bournemouth Univeristy, Talbot Village, BH10 4HY
Abstract#==#Emotion expression is one of the essential traits of conversations. It may be self-related or caused by another speaker. The variety of reasons may serve as a source of the further emotion causes: conversation history, speaker's emotional state, etc. Inspired by the most recent advances in Chain-of-Thought, in this work, we exploit the existing three-hop reasoning approach (THOR) to perform large language model instruction-tuning for answering: emotion states (THOR-state), and emotion caused by one speaker to the other (THOR-cause). We equip THORcause with the reasoning revision (RR) for devising a reasoning path in fine-tuning. In particular, we rely on the annotated speaker emotion states to revise reasoning path. Our final submission, based on Flan-T5-base (250M) and the rule-based span correction technique, preliminary tuned with THOR-state and fine-tuned with THOR-cause-rr on competition training data, results in 3rd and 4th places (F1-proportional) and 5th place (F1-strict) among 15 participating teams. Our THOR implementation fork is publicly available: https://github.com/nicolay-r/THOR-ECAC
Author{1}{Firstname}#=%=#Nicolay
Author{1}{Lastname}#=%=#Rusnachenko
Author{1}{Username}#=%=#nicolay_r
Author{1}{Email}#=%=#rusnicolay@gmail.com
Author{1}{Affiliation}#=%=#Newcastle University
Author{2}{Firstname}#=%=#Huizhi
Author{2}{Lastname}#=%=#Liang
Author{2}{Username}#=%=#okliang
Author{2}{Email}#=%=#oklianghuizi@gmail.com
Author{2}{Affiliation}#=%=#University of Newcastle

==========
èéáğö