Exploring Representations from Unlabeled Data with Co-training for Chinese Word Segmentation
Code (0)
등록된 구현이 없습니다.
Tasks
Chinese Word SegmentationFeature EngineeringSimilar Papers 제목 키워드 기반
Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization
Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect a…
Chinese Word SegmentationSegmentationChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for language understanding. In this work, we …
Language ModelingLanguage ModellingMachine Reading ComprehensionNamed Entity Recognition+5EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering
The availability of clean and diverse labeled data is a major roadblock for training models on complex tasks such as visual question answering (VQA). The extensive work on large vision-and-language models has shown that …
Contrastive LearningOut of Distribution (OOD) DetectionQuestion AnsweringSelf-Supervised Learning+3To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further …
C-Pack: Packed Resources For General Chinese Embeddings
We introduce C-Pack, a package of resources that significantly advance the field of general Chinese embeddings. C-Pack includes three critical resources. 1) C-MTEB is a comprehensive benchmark for Chinese text embeddings…
MTEB Benchmark