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Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation.

, , , and . NAACL-HLT (1), page 4068-4076. Association for Computational Linguistics, (2019)

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Building Program Vector Representations for Deep Learning., , , , , and . KSEM, volume 9403 of Lecture Notes in Computer Science, page 547-553. Springer, (2015)Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation., , , and . NAACL-HLT (1), page 4068-4076. Association for Computational Linguistics, (2019)Order-Planning Neural Text Generation From Structured Data., , , , , , and . AAAI, page 5414-5421. AAAI Press, (2018)Discriminative Neural Sentence Modeling by Tree-Based Convolution., , , , , and . EMNLP, page 2315-2325. The Association for Computational Linguistics, (2015)How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models., , , , , and . ACL (2), page 231-236. Association for Computational Linguistics, (2017)Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models., , and . CoRR, (2017)On End-to-End Program Generation from User Intention by Deep Neural Networks., , , , and . CoRR, (2015)Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path., , , , , and . CoRR, (2015)MCT: a tool for commenting programs by multimedia comments., , , , and . ICSE, page 1339-1342. IEEE Computer Society, (2013)Learning Non-Taxonomic Relations on Demand for Ontology Extension., , , and . International Journal of Software Engineering and Knowledge Engineering, 24 (8): 1159-1176 (2014)