{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/markbert-marking-word-boundaries-improves","title":"MarkBERT: Marking Word Boundaries Improves Chinese BERT","arxiv_id":null,"date":"2021-11-16","proceeding":"ACL ARR November 2021 11","authors":["Anonymous"],"abstract":"We present a Chinese BERT model dubbed MarkBERT that uses word information in this work.\nExisting word-based BERT models regard words as basic units, however,\ndue 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.\nDifferent from existing works, MarkBERT keeps the vocabulary being Chinese characters and inserts boundary markers between contiguous words. \nSuch design enables the model to handle any words in the same way, no matter they are OOV words or not.\nBesides, our model has two additional benefits:\nfirst, it is convenient to add word-level learning objectives over markers, which is complementary to traditional character and sentence-level pretraining tasks;\nsecond, it can easily incorporate richer semantics such as POS tags of words by replacing generic markers with POS tag-specific markers.\nMarkBERT pushes the state-of-the-art of Chinese named entity recognition from 95.4\\% to 96.5\\% on the MSRA dataset and from 82.8\\% to 84.2\\% on the OntoNotes dataset, respectively.\nCompared to previous word-based BERT models, MarkBERT achieves better accuracy on text classification, keyword recognition, and semantic similarity tasks.\\footnote{All the codes and models will be made publicly available at \\url{https://github.com/}}","url_abs":"https://openreview.net/forum?id=7uE-SSLTgxw","url_pdf":"https://openreview.net/pdf?id=7uE-SSLTgxw","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"markbert-marking-word-boundaries-improves","repo_url":"https://github.com/daiyongya/markbert","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"chinese-named-entity-recognition","task_name":"Chinese Named Entity Recognition"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"pos","task_name":"POS"},{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"tag","task_name":"TAG"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}