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ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation.

, , , , and . ACL (1), page 3721-3730. Association for Computational Linguistics, (2019)

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Long-Term Tracking Based on Multi-Feature Adaptive Fusion for Video Target., , , , and . IEICE Transactions, 101-D (5): 1342-1349 (2018)Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation., , , , and . IJCAI, page 4567-4573. ijcai.org, (2018)Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile., , , , and . CoRR, (2019)Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile., , , , and . CCIR, volume 10390 of Lecture Notes in Computer Science, page 3-16. Springer, (2017)Adaptive Bridge between Training and Inference for Dialogue Generation., , , , , and . EMNLP (1), page 2541-2550. Association for Computational Linguistics, (2021)ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation., , , , and . ACL (1), page 3721-3730. Association for Computational Linguistics, (2019)ICTNET at Temporal Summarization Track TREC 2014., , , , , , , and . TREC, Special Publication 500-308, National Institute of Standards and Technology (NIST), (2014)CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation., , , and . EMNLP (1), page 2250-2261. Association for Computational Linguistics, (2021)Tailored Sequence to Sequence Models to Different Conversation Scenarios., , , , and . ACL (1), page 1479-1488. Association for Computational Linguistics, (2018)Effective resistance on graphs and the Epidemic quasimetric, , and . CoRR, (2012)