2021/10/13 by Wenhao Yu, Chenguang Zhu, Yu, Wenhao +14
Computer Science · #Artificial Intelligence (cs.AI) #Artificial intelligence #Benchmark (surveying) #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Focus (optics) #Language model #Linguistics #Machine Learning (cs.LG) #Natural Language Processing Techniques #Natural language processing #Representation (politics) #Semantics (computer science) #Sentence #Sequence labeling #Task (project management) #Text Readability and Simplification #Topic Modeling #Word (group theory) #cs.AI #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.2110.06490
ACL 2022 (Findings)
openalex publication_date 2021/10/13 · arxiv created 2022/03/20 · arxiv updated 2022/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Pre-trained language models (PLMs) aim to learn universal language representations by conducting self-supervised training tasks on large-scale corpora. Since PLMs capture word semantics in different contexts, the quality of word representations highly depends on word frequency, which usually follows a heavy-tailed distributions in the pre-training corpus. Therefore, the embeddings of rare words on the tail are usually poorly optimized. In this work, we focus on enhancing language model pre-training by leveraging definitions of the rare words in dictionaries (e.g., Wiktionary). To incorporate a rare word definition as a part of input, we fetch its definition from the dictionary and append it to the end of the input text sequence. In addition to training with the masked language modeling objective, we propose two novel self-supervised pre-training tasks on word and sentence-level alignment between input text sequence and rare word definitions to enhance language modeling representation with dictionary. We evaluate the proposed Dict-BERT model on the language understanding benchmark GLUE and eight specialized domain benchmark datasets. Extensive experiments demonstrate that Dict-BERT can significantly improve the understanding of rare words and boost model performance on various NLP downstream tasks.