nicolay-r at S em E val-2024 Task 3: Using Flan-T5 for Reasoning Emotion Cause in Conversations with Chain-of-Thought on Emotion States (2024)

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

## methodology we propose a two-stage training mechanism for performing instruction-tuning on large language models (llms), aimed at accurately inferring of instead of directly asking llm the final result at each stage, we exploit the chain-of-thought (cot) concept in the form of the three-hop reasoning (thor) framework (hao et al., 2023). we believe that llm can infer the span that conveys emotion and opinion about it before answering @xmath0. figure 1 illustrates the proposed training methodology, empowered by the cot prompting. we refer to the instruction-tuning mechanisms of the stage 1 and stage 2 as thor state and thor cause respectively. ## datasets and experiential setup we adopt textual resources provided by the competition organizers (wang et al., 2024): training (train json ) and evaluation (test json ) data. within train json , for each conversation, we rely on (i) speakers emotion states, and (ii) emotion causes annotation to compose the datasets d state and d cause , respectively. each dataset represent a list of tuples @xmath1, x, l), where u is an utterance of the conversation context @xmath2. . . u k }, and l is a list of emotion labels, defined as: in the case of d cause , where e u is emotion expressed by u towards u k , or neutral otherwise (e @xmath3. for the particular utterance u, we consider its context as x @xmath4: u idu ′ @xmath5. d cause includes all possible pairs u src , u tgt , where u src @xmath6, and u tgt idu src @xmath7. for the particular pair, we compose the related context (x ′ ) as follows: x ′ = {u ′ : u tgt idu ′ @xmath8. for each pair, we assign @xmath9. we rely on the analysis in for the prompt technique, we use the template «c 1 (x). i(u). choose from e ′ », where i(u) corresponds to the instruction. for d cause we use i(u) = «what emotion causes u text towards the last conversation utterance?» 4 experiments stage 1. figure 2 illustrates the analysis of the f 1 on dev part during the preliminary tuning of flan-t5 base on d state . @xcite we investigate the overfitting after 2 epochs of training. the best state, obtained at the end of the epoch #2 with the f 1(e ′ )= 47.81 on the d state -dev part, has been selected. in further, we refer to this model as flan-t5 base †. stage 2 figure 3 provides a comparative analysis of different fine-tuning techniques. as at the pre-training stage, we investigate the ability to learn task emotion states 2-3 training epoch, followed by overfitting. the official evaluation includes the following f 1 measures: (i) weighted averaged f 1 w * / nonweighted (f 1 * ), and (ii) strict (f 1 s ) / not-strict (f 1 p ) towards predicted spans. to form the submissions for official evaluation, the following span corrections approaches were used: (i) punctuation terms foot_3 exclusion from utterance prefixes and suffixes (by default), and (ii) algorithm-based (section 4.1).