Papers by Ryosuke Kohita
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models (2020.emnlp-main)
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| Challenge: | Existing studies have not investigated the relationship between a token's frequency in the training corpus and syntactic properties models learn about it. |
| Approach: | They develop controlled experiments that probe models’ syntactic nominal number and verbal argument structure generalizations for tokens seen as few as two times during training. |
| Outcome: | The proposed models can make syntactic generalizations for tokens seen as few as two times during training and transfer them to transformed contexts. |
Q-learning with Language Model for Edit-based Unsupervised Summarization (2020.emnlp-main)
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| Challenge: | Unsupervised text summarization methods are promising, but their performance is still behind that of state-of-the-art supervised methods. |
| Approach: | They propose a method based on Q-learning with an edit-based summarization that uses an Editorial Agent and Language Model converter to predict edit actions. |
| Outcome: | The proposed method delivers competitive performance even with zero paired data, while requiring no validation set. |
LOA: Logical Optimal Actions for Text-based Interaction Games (2021.acl-demo)
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Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander Gray
| Challenge: | et al., 2019) have proposed a neuro-symbolic approach for reinforcement learning in non-simultaneous environments. |
| Approach: | They propose an action decision architecture with a neuro-symbolic framework for natural language interaction games. |
| Outcome: | The proposed framework provides an open-source implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. |
Dynamic Feature Selection with Attention in Incremental Parsing (C18-1)
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| Challenge: | Currently, incremental transition-based parsers require that all inputs are visible from the beginning to extract good features from a limited local context. |
| Approach: | They propose a technique to maximize local features with an attention mechanism which works as context- dependent dynamic feature selection. |
| Outcome: | The proposed technique can extract features from a limited local context and is able to perform multilingual experiments and demon strate on local ambiguous points. |
Interactive Construction of User-Centric Dictionary for Text Analytics (2020.acl-main)
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| Challenge: | Existing methods for interactive dictionary construction are limited to a small number of terms, but we propose a method that can be used to create flexible dictionaries with precise granularity. |
| Approach: | They propose a method to construct a term dictionary for text analytics through an interactive process between a human and a machine. |
| Outcome: | The proposed method outperforms baseline methods and works even with a small number of interactions. |
Language-based General Action Template for Reinforcement Learning Agents (2021.findings-acl)
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| Challenge: | Prior knowledge is important in decision-making, and humans preserve it in the form of natural language (NL). |
| Approach: | They propose an environmentagnostic action framework that incorporates prior knowledge into decision-making . they propose to use general semantic schemes to facilitate agent in finding plausible actions . |
| Outcome: | The proposed agent performs better than agents that rely on gamespecific actions. |
Neuro-Symbolic Reinforcement Learning with First-Order Logic (2021.emnlp-main)
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Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante, Michiaki Tatsubori, Asim Munawar, Alexander Gray
| Challenge: | Existing deep reinforcement learning methods require many trials before convergence and no direct interpretability of trained policies is provided. |
| Approach: | They propose a novel RL method which can learn symbolic and interpretable rules in their differentiable network. |
| Outcome: | The proposed method can learn symbolic and interpretable rules in their differentiable network. |
Image Position Prediction in Multimodal Documents (2020.lrec-1)
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| Challenge: | Existing multimodal tasks allow machines to understand images by describing or being asked in natural language. |
| Approach: | They propose a task that predicts the positions of images in a given document . they use a dataset of 66K multimodal documents with 320K images from Wikipedia . |
| Outcome: | The proposed task outperforms baselines while the performance is far from human. |