most citedAssertion-based QA with Question-Aware Open Information Extraction

3 citations · 3 across the 1 of their papers we have counts for

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cs.CL2023

KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation

Jiaqi Bai, Zhao Yan, Jian Yang +3

Existing knowledge-grounded conversation systems generate responses typically in a retrieve-then-generate manner. They require a large knowledge base and a strong knowledge retriev…

cs.CL2023

GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking

Jiaqi Bai, Hongcheng Guo, Jiaheng Liu +4

Retrieval-enhanced text generation has shown remarkable progress on knowledge-intensive language tasks, such as open-domain question answering and knowledge-enhanced dialogue gener…

cs.CL2018

Knowledge Based Machine Reading Comprehension

Yibo Sun, Daya Guo, Duyu Tang +4

Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world. However, existing datasets are typically domin…

cs.CL2018

Keyphrase Generation with Correlation Constraints

Jun Chen, Xiaoming Zhang, Yu Wu +2

In this paper, we study automatic keyphrase generation. Although conventional approaches to this task show promising results, they neglect correlation among keyphrases, resulting i…

cs.CL2018

Table-to-Text: Describing Table Region with Natural Language

Junwei Bao, Duyu Tang, Nan Duan +4

In this paper, we present a generative model to generate a natural language sentence describing a table region, e.g., a row. The model maps a row from a table to a continuous vecto…

cs.CL20183 cited

Assertion-based QA with Question-Aware Open Information Extraction

Zhao Yan, Duyu Tang, Nan Duan +5

We present assertion based question answering (ABQA), an open domain question answering task that takes a question and a passage as inputs, and outputs a semi-structured assertion…