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Researcher profile

Kazuma Hashimoto

7 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Modeling Multi-hop Question Answering as Single Sequence Prediction

    2022 · arXiv (Cornell University)

    Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of …

  2. A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

    2017

    Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in …

  3. Neural Machine Translation with Source-Side Latent Graph Parsing

    2017

    This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser …

  4. A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

    2016 · arXiv (Cornell University)

    Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in …

  5. Tree-to-Sequence Attentional Neural Machine Translation

    2016

    Most of the existing Neural Machine Translation (NMT) models focus on the conversion of sequential data and do not directly use syntactic information. We propose a novel end-to-end syntactic NMT model, extending a sequenceto-sequence model …

  6. Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

    2019 · arXiv (Cornell University)

    Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question. This paper introduces a new graph-based recurrent retrieval approach …

  7. RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

    2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

    Existing KBQA approaches, despite achieving strong performance on i.i.d. test data, often struggle in generalizing to questions involving unseen KB schema items. Prior rankingbased approaches have shown some success in generalization, but suffer from the …