CharBERT: Character-aware Pre-trained Language Model

• URL: http://arxiv.org/abs/2011.01513v1
• Date: Tue, 3 Nov 2020 07:13:06 GMT
• Title: CharBERT: Character-aware Pre-trained Language Model
• Authors: Wentao Ma, Yiming Cui, Chenglei Si, Ting Liu, Shijin Wang, Guoping Hu
• Abstract summary: We propose a character-aware pre-trained language model named CharBERT. We first construct the contextual word embedding for each token from the sequential character representations. We then fuse the representations of characters and the subword representations by a novel heterogeneous interaction module.
• Score: 36.9333890698306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable. However, those methods split a word into subword units and make the representation incomplete and fragile. In this paper, we propose a character-aware pre-trained language model named CharBERT improving on the previous methods (such as BERT, RoBERTa) to tackle these problems. We first construct the contextual word embedding for each token from the sequential character representations, then fuse the representations of characters and the subword representations by a novel heterogeneous interaction module. We also propose a new pre-training task named NLM (Noisy LM) for unsupervised character representation learning. We evaluate our method on question answering, sequence labeling, and text classification tasks, both on the original datasets and adversarial misspelling test sets. The experimental results show that our method can significantly improve the performance and robustness of PLMs simultaneously. Pretrained models, evaluation sets, and code are available at https://github.com/wtma/CharBERT

Related papers

• From Characters to Words: Hierarchical Pre-trained Language Model for Open-vocabulary Language Understanding [22.390804161191635]
  Current state-of-the-art models for natural language understanding require a preprocessing step to convert raw text into discrete tokens. This process known as tokenization relies on a pre-built vocabulary of words or sub-word morphemes. We introduce a novel open-vocabulary language model that adopts a hierarchical two-level approach.
  arXiv  Detail & Related papers  (2023-05-23T23:22:20Z)
• Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained Models [12.0190584907439]
  We propose a new method to exploit word structure and integrate lexical semantics into character representations of pre-trained models. We show that our approach achieves superior performance over the basic pre-trained models BERT, BERT-wwm and ERNIE on different Chinese NLP tasks.
  arXiv  Detail & Related papers  (2022-07-13T02:28:08Z)
• Breaking Character: Are Subwords Good Enough for MRLs After All? [36.11778282905458]
  We pretraining a BERT-style language model over character sequences instead of word-pieces. We compare the resulting model, dubbed TavBERT, against contemporary PLMs based on subwords for three highly complex and ambiguous MRLs. Our results show, for all tested languages, that while TavBERT obtains mild improvements on surface-level tasks, subword-based PLMs achieve significantly higher performance on semantic tasks.
  arXiv  Detail & Related papers  (2022-04-10T18:54:43Z)
• Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens [22.55706811131828]
  We probe the embedding layer of pretrained language models. We show that models learn the internal character composition of whole word and subword tokens.
  arXiv  Detail & Related papers  (2021-08-25T11:48:05Z)
• Charformer: Fast Character Transformers via Gradient-based Subword Tokenization [50.16128796194463]
  We propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model. We introduce a soft gradient-based subword tokenization module (GBST) that automatically learns latent subword representations from characters. We additionally introduce Charformer, a deep Transformer model that integrates GBST and operates on the byte level.
  arXiv  Detail & Related papers  (2021-06-23T22:24:14Z)
• Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment [49.45399359826453]
  Cross-lingual language models are typically pretrained with language modeling on multilingual text or parallel sentences. We introduce denoising word alignment as a new cross-lingual pre-training task. Experimental results show that our method improves cross-lingual transferability on various datasets.
  arXiv  Detail & Related papers  (2021-06-11T13:36:01Z)
• COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining [59.169836983883656]
  COCO-LM is a new self-supervised learning framework that pretrains Language Models by COrrecting challenging errors and COntrasting text sequences. COCO-LM employs an auxiliary language model to mask-and-predict tokens in original text sequences. Our analyses reveal that COCO-LM's advantages come from its challenging training signals, more contextualized token representations, and regularized sequence representations.
  arXiv  Detail & Related papers  (2021-02-16T22:24:29Z)
• Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality [24.80654159288458]
  We propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT. Our module is robust to character-level alterations such as misspellings, word inflection, casing, and punctuation. We show that incorporating our module to mBERT significantly improves the performance on the social media linguistic code-switching evaluation (LinCE) benchmark.
  arXiv  Detail & Related papers  (2020-10-24T01:08:28Z)
• Improving Text Generation with Student-Forcing Optimal Transport [122.11881937642401]
  We propose using optimal transport (OT) to match the sequences generated in training and testing modes. An extension is also proposed to improve the OT learning, based on the structural and contextual information of the text sequences. The effectiveness of the proposed method is validated on machine translation, text summarization, and text generation tasks.
  arXiv  Detail & Related papers  (2020-10-12T19:42:25Z)
• Byte Pair Encoding is Suboptimal for Language Model Pretraining [49.30780227162387]
  We analyze differences between unigram LM tokenization and byte-pair encoding (BPE) We find that the unigram LM tokenization method matches or outperforms BPE across downstream tasks and two languages. We hope that developers of future pretrained LMs will consider adopting the unigram LM method over the more prevalent BPE.
  arXiv  Detail & Related papers  (2020-04-07T21:21:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.