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Deep Reinforcement Learning for Dialogue Generation.

, , , , , and . EMNLP, page 1192-1202. The Association for Computational Linguistics, (2016)

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DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation, , , , , , , , and . (2019)cite arxiv:1911.00536Comment: Accepted by ACL 2020 system demonstration.Hybrid natural language generation for spoken dialogue systems., , and . INTERSPEECH, page 1735-1738. ISCA, (2001)Joint Language and Translation Modeling with Recurrent Neural Networks., , , and . EMNLP, page 1044-1054. ACL, (2013)Probing Factually Grounded Content Transfer with Factual Ablation., , , and . ACL (Findings), page 3732-3746. Association for Computational Linguistics, (2022)Consistent Dialogue Generation with Self-supervised Feature Learning., , , , , , and . CoRR, (2019)Large-scale Expected BLEU Training of Phrase-based Reordering Models., , and . EMNLP, page 1250-1260. ACL, (2014)MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts., , , , , , , , , and . ICLR, OpenReview.net, (2024)Dialogue Response Ranking Training with Large-Scale Human Feedback Data., , , , and . EMNLP (1), page 386-395. Association for Computational Linguistics, (2020)On Available Corpora for Empirical Methods in Vision & Language., , , , , , and . CoRR, (2015)Identifying Agreement and Disagreement in Conversational Speech: Use of Bayesian Networks to Model Pragmatic Dependencies., , , and . ACL, page 669-676. ACL, (2004)