A Unified Query-based Generative Model for Question Generation and Question Answering

By Linfeng Song, Zhiguo Wang, Wael Hamza. 2017

48 citations recorded by the source corpus.

We propose a query-based generative model for solving both tasks of question generation (QG) and question an- swering (QA). The model follows the classic encoder- decoder framework. The encoder takes a passage and a query as input then performs query understanding by matching the query with the passage from multiple per- spectives. The decoder is an attention-based Long Short Term Memory (LSTM) model with copy and coverage mechanisms. In the QG task, a question is generated from the system given the passage and the target answer, whereas in the QA task, the answer is generated given the question and the passage. During the training stage, we leverage a policy-gradient reinforcement learning algorithm to overcome exposure bias, a major prob- lem resulted from sequence learning with cross-entropy loss. For the QG task, our experiments show higher per- formances than the state-of-the-art results. When used as additional training data, the automatically generated questions even improve the performance of a strong ex- tractive QA system. In addition, our model shows bet- ter performance than the state-of-the-art baselines of the generative QA task.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (2019, methodology): “To address the exposure bias problem, some works applied reinforcement learning taking evaluation metrics (e.g., BLEU) as rewards (Song et al., 2017; Kumar et al., 2018).”
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering (2019, neutral): “…(Dehghani et al., 2019) 43.2 54.0 52.9 65.1 - - - - HAS-QA (Pang et al., 2019) 43.2 48.9 62.7 68.7 63.6 68.9 - - BERT (Large) (Nogueira et al., sages with questions, aka inter-sentence matching (Wang and Jiang, 2017; Wang et al., 2016; Seo et al., 2017; Wang et al., 2017; Song et al., 2017).”
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks (2018, methodology): “In addition, our model is more concise compared to most of existing ones, e.g. (Yuan et al., 2017; Song et al., 2017).”
TWEETQA: A Social Media Focused Question Answering Dataset (2019, methodology): “com/shuohangwang/mprc posed generative model (Song et al., 2017) that first encodes the context and question into a multi-perspective memory via four different neural matching layers, then decodes the answer using an attention-based model equipped with both copy”
A Recurrent BERT-based Model for Question Generation (2019, methodology): “Instead of using an annotation vector to tag the answer locations, the (Song et al., 2017) propose to employ a unified framework for QG and question answering by encoding both the answer and the passage with a multi-perspective matching mechanism.”
Learning to Generate Questions by Learning to Recover Answer-containing Sentences (2021, background): “Song et al. (2018) introduced a query-based generative model to jointly solve question generation and answering tasks.”
Simple and Effective Semi-Supervised Question Answering (2018, background): “Several approaches have been suggested for generating nat-ural questions (Tang et al., 2017; Subramanian et al., 2017; Song et al., 2017), however none of them show a signiﬁcant improvement of using the generated questions in a semi-supervised setting.”