  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}}
