Search Results for author: Yuta Koreeda

Found 10 papers, 3 papers with code

ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts

1 code implementation Findings (EMNLP) 2021 Yuta Koreeda, Christopher D. Manning

Reviewing contracts is a time-consuming procedure that incurs large expenses to companies and social inequality to those who cannot afford it.

Multi-Label Classification Natural Language Inference

Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser

1 code implementation EMNLP (NLLP) 2021 Yuta Koreeda, Christopher D. Manning

While many NLP pipelines assume raw, clean texts, many texts we encounter in the wild, including a vast majority of legal documents, are not so clean, with many of them being visually structured documents (VSDs) such as PDFs.

Boundary Detection

Hitachi at MRP 2020: Text-to-Graph-Notation Transducer

no code implementations CONLL 2020 Hiroaki Ozaki, Gaku Morio, Yuta Koreeda, Terufumi Morishita, Toshinori Miyoshi

This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages.

Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization

no code implementations ACL 2020 Gaku Morio, Hiroaki Ozaki, Terufumi Morishita, Yuta Koreeda, Kohsuke Yanai

Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges.

Argument Mining

StruAP: A Tool for Bundling Linguistic Trees through Structure-based Abstract Pattern

no code implementations EMNLP 2017 Kohsuke Yanai, Misa Sato, Toshihiko Yanase, Kenzo Kurotsuchi, Yuta Koreeda, Yoshiki Niwa

We present a tool for developing tree structure patterns that makes it easy to define the relations among textual phrases and create a search index for these newly defined relations.

Decision Making Information Retrieval +1

bunji at SemEval-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility Features

no code implementations SEMEVAL 2017 Yuta Koreeda, Takuya Hashito, Yoshiki Niwa, Misa Sato, Toshihiko Yanase, Kenzo Kurotsuchi, Kohsuke Yanai

This paper describes a text-ranking system developed by bunji team in SemEval-2017 Task 3: Community Question Answering, Subtask A and C. The goal of the task is to re-rank the comments in a question-and-answer forum such that useful comments for answering the question are ranked high.

Community Question Answering Question Similarity

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