42 citations · 113 across the 8 of their papers we have counts for
8 papers
Chat, Shift and Perform: Bridging the Gap between Task-oriented and Non-task-oriented Dialog Systems
Teppei Yoshino, Yosuke Fukuchi, Shoya Matsumori +1
We propose CASPER (ChAt, Shift and PERform), a novel dialog system consisting of three types of dialog models: chatter, shifter, and performer. Shifter, which is designed for topic…
Mask and Cloze: Automatic Open Cloze Question Generation using a Masked Language Model
Shoya Matsumori, Kohei Okuoka, Ryoichi Shibata +3
Open cloze questions have been attracting attention for both measuring the ability and facilitating the learning of L2 English learners. In spite of its benefits, the open cloze te…
LatteGAN: Visually Guided Language Attention for Multi-Turn Text-Conditioned Image Manipulation
Shoya Matsumori, Yuki Abe, Kosuke Shingyouchi +2
Text-guided image manipulation tasks have recently gained attention in the vision-and-language community. While most of the prior studies focused on single-turn manipulation, our g…
d3rlpy: An Offline Deep Reinforcement Learning Library
Takuma Seno, Michita Imai
In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-…
Unified Questioner Transformer for Descriptive Question Generation in Goal-Oriented Visual Dialogue
Shoya Matsumori, Kosuke Shingyouchi, Yuki Abe +3
Building an interactive artificial intelligence that can ask questions about the real world is one of the biggest challenges for vision and language problems. In particular, goal-o…
SLAM-Inspired Simultaneous Contextualization and Interpreting for Incremental Conversation Sentences
Yusuke Takimoto, Yosuke Fukuchi, Shoya Matsumori +1
Distributed representation of words has improved the performance for many natural language tasks. In many methods, however, only one meaning is considered for one label of a word,…
Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents
Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa +1
In cooperation, the workers must know how co-workers behave. However, an agent's policy, which is embedded in a statistical machine learning model, is hard to understand, and requi…
Bayesian Inference of Self-intention Attributed by Observer
Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa +2
Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging. We belie…