Papers › An Empirical Study of Building a Strong Baseline for Constituency Parsing

An Empirical Study of Building a Strong Baseline for Constituency Parsing

1 Jul 2018ACL 2018 7archive 2025-07-28

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

This 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.

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Tasks

Abstractive Text Summarization Constituency Parsing Machine Translation Text Generation Translation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Constituency Parsing	Penn Treebank	LSTM Encoder-Decoder + LSTM-LM	F1 score	94.32	#18 of 27	Archive leaderboard	report

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