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Aoba_v3 bot: a multimodal chatbot system combining rules and various response generation models.

, , , , , , , , , and . Adv. Robotics, 37 (21): 1392-1405 (November 2023)

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Aoba_v3 bot: a multimodal chatbot system combining rules and various response generation models., , , , , , , , , and . Adv. Robotics, 37 (21): 1392-1405 (November 2023)Generating Stylistically Consistent Dialog Responses with Transfer Learning., , , , and . IJCNLP(2), page 408-412. Asian Federation of Natural Language Processing, (2017)Dialogue System Live Competition: Identifying Problems with Dialogue Systems Through Live Event., , , , , and . IWSDS, volume 714 of Lecture Notes in Electrical Engineering, page 185-199. Springer, (2019)A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems., , , and . CoRR, (2024)Utterance Pair Scoring for Noisy Dialogue Data Filtering., , , and . CoRR, (2020)Bipartite-play Dialogue Collection for Practical Automatic Evaluation of Dialogue Systems., , , , , and . AACL/IJCNLP 2022 (Student Research Workshop), page 8-16. Association for Computational Linguistics, (2022)Target-Guided Open-Domain Conversation Planning., , , , , and . COLING, page 660-668. International Committee on Computational Linguistics, (2022)Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness., , , and . EMNLP (1), page 941-958. Association for Computational Linguistics, (2020)Evaluating Dialogue Generation Systems via Response Selection., , , , and . ACL, page 593-599. Association for Computational Linguistics, (2020)N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models., , , , , and . SIGDIAL, page 637-644. Association for Computational Linguistics, (2022)