1 code implementation • ACL 2021 • Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander Gray
We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games.
no code implementations • EMNLP 2021 • Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante, Michiaki Tatsubori, Asim Munawar, Alexander Gray
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided.
no code implementations • 3 Mar 2021 • Daiki Kimura, Subhajit Chaudhury, Akifumi Wachi, Ryosuke Kohita, Asim Munawar, Michiaki Tatsubori, Alexander Gray
Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide.
no code implementations • CoNLL (EMNLP) 2021 • Ran Iwamoto, Ryosuke Kohita, Akifumi Wachi
Particularly, the latest approaches such as hyperbolic embeddings showed significant performance by representing essential meanings in a hierarchy (generality and similarity of objects) with spatial properties (distance from the origin and difference of angles).
no code implementations • EMNLP 2020 • Ethan Wilcox, Peng Qian, Richard Futrell, Ryosuke Kohita, Roger Levy, Miguel Ballesteros
Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts.
1 code implementation • EMNLP 2020 • Ryosuke Kohita, Akifumi Wachi, Yang Zhao, Ryuki Tachibana
Q-learning is leveraged to train the agent to produce proper edit actions.
no code implementations • ACL 2020 • Ryosuke Kohita, Issei Yoshida, Hiroshi Kanayama, Tetsuya Nasukawa
We propose a methodology to construct a term dictionary for text analytics through an interactive process between a human and a machine, which helps the creation of flexible dictionaries with precise granularity required in typical text analysis.
no code implementations • LREC 2020 • Masayasu Muraoka, Ryosuke Kohita, Etsuko Ishii
Datasets for these tasks contain a large number of pairs of an image and the corresponding sentence as an instance.
no code implementations • COLING 2018 • Ryosuke Kohita, Hiroshi Noji, Yuji Matsumoto
One main challenge for incremental transition-based parsers, when future inputs are invisible, is to extract good features from a limited local context.
no code implementations • WS 2018 • Hiroshi Kanayama, Masayasu Muraoka, Ryosuke Kohita
This paper demonstrates a neural parser implementation suitable for consistently head-final languages such as Japanese.
no code implementations • WS 2017 • Ryosuke Kohita, Hiroshi Noji, Yuji Matsumoto
We present a new transition system with word reordering for unrestricted non-projective dependency parsing.
1 code implementation • EACL 2017 • Ryosuke Kohita, Hiroshi Noji, Yuji Matsumoto
Universal Dependencies (UD) is becoming a standard annotation scheme cross-linguistically, but it is argued that this scheme centering on content words is harder to parse than the conventional one centering on function words.