413 citations · 480 across the 8 of their papers we have counts for
8 papers
Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures
M. Li, H. Bai, L. Tan +2
Measuring sentence semantic similarity using pre-trained language models such as BERT generally yields unsatisfactory zero-shot performance, and one main reason is ineffective toke…
Don't Change Me! User-Controllable Selective Paraphrase Generation
Mohan Zhang, Luchen Tan, Zhengkai Tu +4
In the paraphrase generation task, source sentences often contain phrases that should not be altered. Which phrases, however, can be context dependent and can vary by application.…
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…
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-…
Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
Ruixue Zhang, Wei Yang, Luyun Lin +7
Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations…
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering
Wei Yang, Yuqing Xie, Luchen Tan +3
Recently, a simple combination of passage retrieval using off-the-shelf IR techniques and a BERT reader was found to be very effective for question answering directly on Wikipedia,…
End-to-End Open-Domain Question Answering with BERTserini
Wei Yang, Yuqing Xie, Aileen Lin +5
We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit. In contrast to most question answering and…
Neural Contextual Conversation Learning with Labeled Question-Answering Pairs
Kun Xiong, Anqi Cui, Zefeng Zhang +1
Neural conversational models tend to produce generic or safe responses in different contexts, e.g., reply \textit{"Of course"} to narrative statements or \textit{"I don't know"} to…