Improving Question Generation With to the Point Context

Abstract

Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence. A major challenge in QG is to identify answer-relevant context words to finish the declarative-to-interrogative sentence transformation. Existing sequence-to-sequence neural models achieve this goal by proximity-based answer position encoding under the intuition that neighboring words of answers are of high possibility to be answer-relevant. However, such intuition may not apply to all cases especially for sentences with complex answerrelevant relations. Consequently, the performance of these models drops sharply when the relative distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question increases. To address this issue, we propose a method to jointly model the unstructured sentence and the structured answer-relevant relation (extracted from the sentence in advance) for question generation. Specifically, the structured answer-relevant relation acts as the to the point context and it thus naturally helps keep the generated question to the point, while the unstructured sentence provides the full information. Extensive experiments show that to the point context helps our question generation model achieve significant improvements on several automatic evaluation metrics. Furthermore, our model is capable of generating diverse questions for a sentence which conveys multiple relations of its answer fragment.

How to Ask Good Questions? Try to Leverage Paraphrases

“…In order to make use of the context information of paragraphs, Zhao et al (2018b) propose a gated self-attention network to encode context passage. Based on this, Zhang and Bansal (2019) apply reinforcement learning to deal with semantic drift in QG; Nema et al (2019) use a passage-answer fusion mechanism to obtain answer-focused context representations; Li et al (2019a) utilize gated attention to fuse answer-relevant relation with context sentence. Besides, Chen et al (2019) design different passage graphs to capture structure information of passage through graph neural networks.…”

“…On SQuAD, since there are two different splits that are most often used, we conduct experiments on both two splits on sentence-level. For Du Split (Du et al, 2017), we use the same settings with Li et al (2019a) and there are 74689, 10427 and 11609 sentence-question-answer triples for training, validation and test respectively. For Zhou Split , we use the data shared by and there are 86,635, 8,965 and 8,964 triples correspondingly.…”

“…Sent-Relation (Li et al, 2019a): extracts answer-relevant relations in sentence and encodes both sentence and relations to capture answerfocused representations.…”

PathQG: Neural Question Generation from Facts

“…As follow-up, ; Sun et al (2018); Kim et al (2019) propose to utilize the answers to decrease the generation uncertainty. Meanwhile, and Li et al (2019) explore to use answer-relevant context to guide question generation. Besides, some studies (Wang et al, 2017;Wang et al, 2019) take question generation as a subtask, and jointly learn it with other tasks, such as question answering and phrase extraction, which also help to alleviate the uncertainty and improve the generation performance.…”

Review-based Question Generation with Adaptive Instance Transfer and Augmentation

“…Question generation (QG) is an emerging research topic due to its wide application scenarios such as education , goal-oriented dialogue (Lee et al, 2018), and question answering . The preliminary neural QG models outperform the rule-based methods relying on hand-craft features, and thereafter various models have been proposed to further improve the performance via incorporating question type (Dong et al, 2018), answer position , long passage modeling (Zhao et al, 2018b), question difficulty , and to the point context (Li et al, 2019). Some works try to find the possible answer text spans for facilitating the learning .…”