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This paper presents new state-of-the-art models for three tasks,part-of-speech tagging, syntactic parsing, and semantic parsing, using thecutting-edge contextualized embedding framework known as BERT. For each task,we first replicate and simplify the current state-of-the-art approach toenhance its model efficiency. We then evaluate our simplified approaches onthose three tasks using token embeddings generated by BERT. 12 datasets in bothEnglish and Chinese are used for our experiments. The BERT models outperformthe previously best-performing models by 2.5% on average (7.5% for the mostsignificant case). Moreover, an in-depth analysis on the impact of BERTembeddings is provided using self-attention, which helps understanding in thisrich yet representation. All models and source codes are available in public sothat researchers can improve upon and utilize them to establish strongbaselines for the next decade.

Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT