Kyosuke Nishida

Understanding the Semantic Structures of Tables with a Hybrid Deep Neural Network Architecture

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

We propose a new deep neural network architecture, TabNet, for table type classification. Table type is essential information for exploring the power of Web tables, and it is important to understand the semantic structures of …

A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension

2019 · arXiv (Cornell University)

Conversational machine comprehension (CMC) requires understanding the context of multi-turn dialogue. Using BERT, a pre-training language model, has been successful for single-turn machine comprehension, while modeling multiple turns of question answering with BERT has not …

Multi-style Generative Reading Comprehension

2019

Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.