Papers › MarkBERT: Marking Word Boundaries Improves Chinese BERT

MarkBERT: Marking Word Boundaries Improves Chinese BERT

12 Mar 2022arXiv:2203.06378archive 2025-07-28

Linyang Li, Yong Dai, Duyu Tang, Xipeng Qiu, Zenglin Xu, Shuming Shi

We present a Chinese BERT model dubbed MarkBERT that uses word information in this work. Existing word-based BERT models regard words as basic units, however, due to the vocabulary limit of BERT, they only cover high-frequency words and fall back to character level when encountering out-of-vocabulary (OOV) words. Different from existing works, MarkBERT keeps the vocabulary being Chinese characters and inserts boundary markers between contiguous words. Such design enables the model to handle any words in the same way, no matter they are OOV words or not. Besides, our model has two additional benefits: first, it is convenient to add word-level learning objectives over markers, which is complementary to traditional character and sentence-level pretraining tasks; second, it can easily incorporate richer semantics such as POS tags of words by replacing generic markers with POS tag-specific markers. With the simple markers insertion, MarkBERT can improve the performances of various downstream tasks including language understanding and sequence labeling. \footnote{All the codes and models will be made publicly available at \url{https://github.com/daiyongya/markbert}}

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Tasks

Chinese Named Entity Recognition Named Entity Recognition (NER)POS Semantic Similarity Semantic Textual Similarity Sentence TAG Text Classification named-entity-recognition text-classification

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Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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