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Researcher profile

Yutaka Matsuo

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Refining Raw Sentence Representations for Textual Entailment Recognition via Attention

    2017 · arXiv (Cornell University)

    In this paper we present the model used by the team Rivercorners for the 2017 RepEval shared task. First, our model separately encodes a pair of sentences into variable-length representations by using a bidirectional LSTM. …

  2. Replication issues in syntax-based aspect extraction for opinion mining

    2017

    Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study …

  3. Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

    2021 · arXiv (Cornell University)

    Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In particular, we still do not have agreeable ways to measure …

  4. Generalized Decision Transformer for Offline Hindsight Information Matching

    2021 · arXiv (Cornell University)

    How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for practical applications. Recent works have shown that …

  5. Evaluating Large Language Models’ Understanding of Financial Terminology via Definition Modeling

    2023

    James Jhirad, Edison Marrese-Taylor, Yutaka Matsuo. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Student Research Workshop. 2023.

  6. Large Language Models are Zero-Shot Reasoners

    2022 · arXiv (Cornell University)

    Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique …