36 citations · 53 across the 2 of their papers we have counts for
6 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…
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…