An Empirical Study of Building a Strong Baseline for Constituency Parsing

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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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Constituency Parsing Penn Treebank LSTM Encoder-Decoder + LSTM-LM F1 score 94.32 # 13

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