no code implementations • 21 Jan 2020 • Itsumi Saito, Kyosuke Nishida, Kosuke Nishida, Atsushi Otsuka, Hisako Asano, Junji Tomita, Hiroyuki Shindo, Yuji Matsumoto
Unlike the previous models, our length-controllable abstractive summarization model incorporates a word-level extractive module in the encoder-decoder model instead of length embeddings.
no code implementations • ACL 2019 • Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, Junji Tomita
It enables QFE to consider the dependency among the evidence sentences and cover important information in the question sentence.
Ranked #61 on Question Answering on HotpotQA
no code implementations • ACL 2019 • Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita
Second, whereas previous studies built a specific model for each answer style because of the difficulty of acquiring one general model, our approach learns multi-style answers within a model to improve the NLG capability for all styles involved.
Ranked #1 on Question Answering on MS MARCO
no code implementations • 31 Aug 2018 • Kyosuke Nishida, Itsumi Saito, Atsushi Otsuka, Hisako Asano, Junji Tomita
Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages.