37 citations · 94 across the 4 of their papers we have counts for
7 papers
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
Andy T. Liu, Wei Xiao, Henghui Zhu +3
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label ef…
Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey
Xiaokai Wei, Shen Wang, Dejiao Zhang +2
Pretrained Language Models (PLM) have established a new paradigm through learning informative contextualized representations on large-scale text corpus. This new paradigm has revol…
Improving Factual Consistency of Abstractive Summarization via Question Answering
Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu +7
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The f…
Supporting Clustering with Contrastive Learning
Dejiao Zhang, Feng Nan, Xiaokai Wei +6
Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space. However, different categories often ove…
Entity-level Factual Consistency of Abstractive Text Summarization
Feng Nan, Ramesh Nallapati, Zhiguo Wang +5
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models t…
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
Yifan Gao, Henghui Zhu, Patrick Ng +7
In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system ne…