CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation (2403.07260v1)
Abstract: Emotion recognition in conversation (ERC) is a task which predicts the emotion of an utterance in the context of a conversation. It tightly depends on dialogue context, speaker identity information, multiparty dialogue scenario and so on. However, the state-of-the-art method (instructERC) solely identifying speaker, and ignores commonsense knowledge(i.e., reaction of the listeners and intention of the speaker, etc.) behind speakers during a conversation, which can deeply mine speaker information. To this end, we propose a novel joint LLMs with commonsense knowledge framework for emotion recognition in conversation, namely CKERC.We design prompts to generate interlocutors' commonsense based on historical utterances with LLM. And we use the interlocutor commonsense identification task for LLM pre-training to fine-tune speaker implicit clues information.By solving above challenge, our method achieve state-of-the-art.We extensive experiment on three widely-used datasets, i.e., IEMOCAP, MELD, EmoryNLP, demonstrate our method superiority. Also, we conduct in-depth analysis and further demonstrate the effectiveness of commonsense knowledge in ERC task in LLM.
- Comet: Commonsense transformers for automatic knowledge graph construction.
- Iemocap: Interactive emotional dyadic motion capture database. Language resources and evaluation, 42:335–359.
- Jiawen Deng and Fuji Ren. 2021. A survey of textual emotion recognition and its challenges. IEEE Transactions on Affective Computing.
- Cosmic: Commonsense knowledge for emotion identification in conversations. In Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020, pages 2470–2481. Association for Computational Linguistics (ACL).
- Conversational memory network for emotion recognition in dyadic dialogue videos. In Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting, volume 2018, page 2122. NIH Public Access.
- Supervised adversarial contrastive learning for emotion recognition in conversations. arXiv preprint arXiv:2306.01505.
- Lora: Low-rank adaptation of large language models.
- Relation-aware graph attention networks with relational position encodings for emotion recognition in conversations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7360–7370.
- Higru: Hierarchical gated recurrent units for utterance-level emotion recognition. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 397–406.
- Instructerc: Reforming emotion recognition in conversation with a retrieval multi-task llms framework. arXiv preprint arXiv:2309.11911.
- Past, present, and future: Conversational emotion recognition through structural modeling of psychological knowledge. In Findings of the association for computational linguistics: EMNLP 2021, pages 1204–1214.
- Hitrans: A transformer-based context-and speaker-sensitive model for emotion detection in conversations. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4190–4200.
- Dialoguernn: An attentive rnn for emotion detection in conversations. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 6818–6825.
- Conversation understanding using relational temporal graph neural networks with auxiliary cross-modality interaction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15154–15167.
- A survey on deep learning for textual emotion analysis in social networks. Digital Communications and Networks, 8(5):745–762.
- Meld: A multimodal multi-party dataset for emotion recognition in conversations.
- Atomic: An atlas of machine commonsense for if-then reasoning. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 3027–3035.
- Dialogxl: All-in-one xlnet for multi-party conversation emotion recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 13789–13797.
- A discourse-aware graph neural network for emotion recognition in multi-party conversation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2949–2958.
- Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
- Context-and sentiment-aware networks for emotion recognition in conversation. IEEE Transactions on Artificial Intelligence, 3(5):699–708.
- Contextual information and commonsense based prompt for emotion recognition in conversation. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 707–723. Springer.
- Sayyed M Zahiri and Jinho D Choi. 2018. Emotion detection on tv show transcripts with sequence-based convolutional neural networks. In Workshops at the thirty-second aaai conference on artificial intelligence.
- Cauain: Causal aware interaction network for emotion recognition in conversations. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI, pages 4524–4530.
- Topic-driven and knowledge-aware transformer for dialogue emotion detection.
- Yumeng Fu (3 papers)