Question Generation for Question Answering
Nan Duan,Duyu Tang,Peng Chen,Ming Zhou +3 more
- pp 866-874
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TLDR
Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.Abstract:
This paper presents how to generate questions from given passages using neural networks, where large scale QA pairs are automatically crawled and processed from Community-QA website, and used as training data The contribution of the paper is 2-fold: First, two types of question generation approaches are proposed, one is a retrieval-based method using convolution neural network (CNN), the other is a generation-based method using recurrent neural network (RNN); Second, we show how to leverage the generated questions to improve existing question answering systems We evaluate our question generation method for the answer sentence selection task on three benchmark datasets, including SQuAD, MS MARCO, and WikiQA Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achievedread more
Citations
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Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks
TL;DR: A maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation, which outperforms previous approaches with either sentence-level or paragraph-level inputs.
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Event Extraction as Machine Reading Comprehension.
TL;DR: This paper proposes a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC), which includes an unsupervised question generation process, which can transfer event schema into a set of natural questions, followed by a BERT-based question-answering process to retrieve answers as EE results.
Proceedings ArticleDOI
Answer-focused and Position-aware Neural Question Generation.
TL;DR: The experimental results show that the proposed answer-focused and position-aware neural question generation model significantly improves the baseline and outperforms the state-of-the-art system.
Proceedings ArticleDOI
Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia
Xinya Du,Claire Cardie +1 more
TL;DR: This article proposed a neural network approach that incorporates coreference knowledge via a novel gating mechanism to generate question-answer pairs that cover content beyond a single sentence and found that the linguistic knowledge introduced by the coreference representation aids question generation significantly.
Proceedings ArticleDOI
Generating Clarifying Questions for Information Retrieval
TL;DR: A taxonomy of clarification for open-domain search queries is identified by analyzing large-scale query reformulation data sampled from Bing search logs, and supervised and reinforcement learning models for generating clarifying questions learned from weak supervision data are proposed.
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
TL;DR: The Stanford Question Answering Dataset (SQuAD) as mentioned in this paper is a reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage.