87 citations · 311 across the 13 of their papers we have counts for
24 papers
End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task
Peng Shi, Tao Yu, Patrick Ng +1
In this work, we focus on two crucial components in the cross-domain text-to-SQL semantic parsing task: schema linking and value filling. To encourage the model to learn better enc…
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…
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…
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…
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…
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
Siamak Shakeri, Cicero Nogueira dos Santos, Henry Zhu +5
We propose an end-to-end approach for synthetic QA data generation. Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate bo…