  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 THOR-cause 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
