Examining Dialogue Consistency Based on Chapter-Level Semantic Graph
Li Fei(),Deng Kaifang,Fan Maohui,Teng Chong,Ji Donghong
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
[Objective] This paper integrates chapter-level semantic graphs to improve the accuracy of dialogue consistency detection. [Methods] First, we used the pre-trained language model BERT to encode the dialogue context and knowledge base. Then, we constructed a dialogue chapter-level semantic graph containing coreference chains and abstract meaning representations. Third, we captured the semantic information of the constructed graph using a multi-relation graph convolutional network. Finally, we built multiple classifiers to predict dialogue inconsistency. [Results] We examined our new model on the CI-ToD benchmark dataset and compared its performance with the existing models. The proposed model’s F1 value improved by more than 1% over the optimal models. [Limitations] The proposed model cannot address the co-referential entity omission in dialogues. [Conclusions] Integrating various types of semantic information, such as coreference chains and abstract meaning representations, can effectively improve the performance of dialogue consistency detection.
李霏, 邓凯方, 范茂慧, 滕冲, 姬东鸿. 基于篇章级语义图的对话一致性检测*[J]. 数据分析与知识发现, 2024, 8(5): 18-28.
Li Fei, Deng Kaifang, Fan Maohui, Teng Chong, Ji Donghong. Examining Dialogue Consistency Based on Chapter-Level Semantic Graph. Data Analysis and Knowledge Discovery, 2024, 8(5): 18-28.
Eric M, Krishnan L, Charette F, et al. Key-Value Retrieval Networks for Task-Oriented Dialogue[C]// Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue. 2017: 37-49.
[2]
Qin L B, Che W X, Li Y M, et al. DCR-Net: A Deep Co-interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020: 8665-8672.
[3]
Qin L B, Liu Y J, Che W X, et al. Entity-Consistent End-to-End Task-Oriented Dialogue System with KB Retriever[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019: 133-142.
[4]
Wu C S, Madotto A, Hosseini-Asl E, et al. Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 808-819.
[5]
Takanobu R, Liang R Z, Huang M L. Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 625-638.
[6]
Zhang S Z, Dinan E, Urbanek J, et al. Personalizing Dialogue Agents: I Have a Dog, Do You Have Pets Too?[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers). 2018: 2204-2213.
[7]
Qin L B, Xu X, Che W X, et al. Dynamic Fusion Network for Multi-Domain End-to-End Task-Oriented Dialog[C] // Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 6344-6354.
[8]
Li Y M, Yao K S, Qin L B, et al. Slot-Consistent NLG for Task-Oriented Dialogue Systems with Iterative Rectification Network[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 97-106.
[9]
Wu C S, Hoi S C H, Socher R, et al. TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020: 917-929.
[10]
Wang J, Liu J H, Bi W, et al. Dual Dynamic Memory Network for End-to-End Multi-Turn Task-Oriented Dialog Systems[C]// Proceedings of the 28th International Conference on Computational Linguistics. 2020: 4100-4110.
[11]
Zheng Y H, Chen G Y, Huang M L, et al. Personalized Dialogue Generation with Diversified Traits[OL]. arXiv Preprint, arXiv: 1901.09672.
[12]
Welleck S, Weston J, Szlam A, et al. Dialogue Natural Language Inference[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2020: 3731-3741.
[13]
Song H Y, Wang Y, Zhang W N, et al. Profile Consistency Identification for Open-Domain Dialogue Agents[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020: 6651-6662.
[14]
Nie Y X, Williamson M, Bansal M, et al. I Like Fish, Especially Dolphins: Addressing Contradictions in Dialogue Modeling[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1:Long Papers). 2021: 1699-1713.
[15]
Qin L B, Xie T B, Huang S J, et al. Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-Oriented Dialogue System[C]// Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021: 2357-2367.
[16]
Lee K, He L H, Lewis M, et al. End-to-End Neural Coreference Resolution[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 188-197.
[17]
Bai X F, Chen Y L, Song L F, et al. Semantic Representation for Dialogue Modeling[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1:Long Papers). 2021: 4430-4445.
[18]
Qin L B, Chen Q G, Xie T B, et al. CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-Oriented Dialogue[C]// Proceedings of the 29th International Conference on Computational Linguistics. 2022: 461-470.
[19]
Madotto A, Wu C S, Fung P. Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers). 2018: 1468-1478.
[20]
OpenAI. ChatGPT: Optimizing Language Models for Dialogue[EB/OL]. [2023-05-08]. https://openai.com/blog/chatgpt/.
[21]
Wu X H, Wang T R, Fan Y P, et al. Chinese Event Extraction via Graph Attention Network[J]. ACM Transactions on Asian and Low-Resource Language Information Processing, 2022, 21(4): 71.
[22]
Liu X, Luo Z C, Huang H Y. Jointly Multiple Events Extraction via Attention-Based Graph Information Aggregation[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 1247-1256.
[23]
Kipf T N, Welling M. Semi-Supervised Classification with Graph Convolutional Networks[OL]. arXiv Preprint, arXiv: 1609.02907.
[24]
Chen Q, Zhu X D, Ling Z H, et al. Enhanced LSTM for Natural Language Inference[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers). 2017: 1657-1668.
[25]
Conneau A, Kiela D, Schwenk H, et al. Supervised Learning of Universal Sentence Representations from Natural Language Inference Data[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 670-680.
[26]
Yang R Q, Zhang J H, Gao X, et al. Simple and Effective Text Matching with Richer Alignment Features[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 4699-4709.
[27]
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]// 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). 2019: 4171-4186.
[28]
Liu Y H, Ott M, Goyal N, et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach[OL]. arXiv Preprint, arXiv:1907.11692.
[29]
Yang Z L, Dai Z H, Yang Y M, et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding[C]// Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019: 5753-5763.
[30]
Beltagy I, Peters M E, Cohan A. Longformer: The Long-Document Transformer[OL]. arXiv Preprint, arXiv: 2004.05150.
[31]
Lewis M, Liu Y H, Goyal N, et al. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 7871-7880.