27 citations · 48 across the 6 of their papers we have counts for
13 papers
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge
Xingyu Fu, Sheng Zhang, Gukyeong Kwon +10
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge. Recently, pre-trained Langua…
UNITE: A Unified Benchmark for Text-to-SQL Evaluation
Wuwei Lan, Zhiguo Wang, Anuj Chauhan +15
A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures. To comprehensively e…
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…
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…