5 citations · 15 across the 11 of their papers we have counts for
5 papers · 1 filter
Can Offline Reinforcement Learning Help Natural Language Understanding?
Ziqi Zhang, Yile Wang, Yue Zhang +1
Pre-training has been a useful method for learning implicit transferable knowledge and it shows the benefit of offering complementary features across different modalities. Recent w…
Pre-Training a Graph Recurrent Network for Language Representation
Yile Wang, Linyi Yang, Zhiyang Teng +2
Transformer-based pre-trained models have gained much advance in recent years, becoming one of the most important backbones in natural language processing. Recent work shows that t…
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Jiangbin Zheng, Yile Wang, Ge Wang +5
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e.g., Skip-gram, Word2Vec) still play an…
YATO: Yet Another deep learning based Text analysis Open toolkit
Zeqiang Wang, Yile Wang, Jiageng Wu +2
We introduce YATO, an open-source, easy-to-use toolkit for text analysis with deep learning. Different from existing heavily engineered toolkits and platforms, YATO is lightweight…
Lost in Context? On the Sense-wise Variance of Contextualized Word Embeddings
Yile Wang, Yue Zhang
Contextualized word embeddings in language models have given much advance to NLP. Intuitively, sentential information is integrated into the representation of words, which can help…