13 citations · 23 across the 9 of their papers we have counts for
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cs.CL2020★ 6 cited
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Peng Shi, Patrick Ng, Zhiguo Wang +5
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural langua…
cs.CL2020★ 1 cited
Segatron: Segment-Aware Transformer for Language Modeling and Understanding
He Bai, Peng Shi, Jimmy Lin +5
Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it…
cs.CL2020
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2
He Bai, Peng Shi, Jimmy Lin +5
The semantics of a text is manifested not only by what is read, but also by what is not read. In this article, we will study how the implicit "not read" information such as end-of-…