SemSeq: A Regime for Training Widely-Applicable Word-Sequence Encoders - researchr publication authors

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Hiroaki Tsuyuki, Tetsuji Ogawa, Tetsunori Kobayashi, Yoshihiko Hayashi. SemSeq: A Regime for Training Widely-Applicable Word-Sequence Encoders. In Le Minh Nguyen, Xuan Hieu Phan, Kôiti Hasida, Satoshi Tojo, editors, Computational Linguistics - 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Hanoi, Vietnam, October 11-13, 2019, Revised Selected Papers. Volume 1215 of Communications in Computer and Information Science, pages 43-55, Springer, 2019. [doi]

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