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Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels.

, , and . EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 4208-4220. Association for Computational Linguistics, (2020)

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Insertion Transformer: Flexible Sequence Generation via Insertion Operations., , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 5976-5985. PMLR, (2019)Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels., , and . EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 4208-4220. Association for Computational Linguistics, (2020)Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation., , , , and . CoRR, (2021)Modular Length Control for Sentence Generation., , , and . ESANN, page 607-612. (2020)DOM-Q-NET: Grounded RL on Structured Language., , and . ICLR (Poster), OpenReview.net, (2019)Contextual Lensing of Universal Sentence Representations.. CoRR, (2020)Graph Normalizing Flows., , , , and . NeurIPS, page 13556-13566. (2019)An Empirical Study of Generation Order for Machine Translation., , , and . EMNLP (1), page 5764-5773. Association for Computational Linguistics, (2020)VSE++: Improving Visual-Semantic Embeddings with Hard Negatives., , , and . BMVC, page 12. BMVA Press, (2018)Towards Generalizable Sentence Embeddings., , , and . Rep4NLP@ACL, page 239-248. Association for Computational Linguistics, (2016)