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2018 ACL ACL 2018

An Empirical Study of Building a Strong Baseline for Constituency Parsing

Abstract

AbstractThis paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. We incorporate several techniques that were mainly developed in natural language generation tasks, e.g., machine translation and summarization, and demonstrate that the sequence-to-sequence model achieves the current top-notch parsers’ performance (almost) without requiring any explicit task-specific knowledge or architecture of constituent parsing.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — neural parsing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
🐣 Hot Topic Early Bird — constituency parsing

Authors

Jun Suzuki , Sho Takase , Hidetaka Kamigaito , Makoto Morishita , Masaaki Nagata

Topics

Natural Language Processing > Understanding > Parsing Natural Language Processing > Applications > Machine Translation Machine Learning > Learning Types > Transfer Learning Deep Learning > Learning Types > Deep Learning Deep Learning > Learning Types > Sequence Modeling

Keywords

machine translation natural language generation empirical study constituency parsing sequence-to-sequence model neural parsing neural network
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