ConQuest: Contextual Question Paraphrasing through Answer-Aware Synthetic Question Generation
Year: 2021
Type: conference-paper
Abstract: Despite excellent performance on tasks such as question answering, Transformer-based architectures remain sensitive to syntactic and contextual ambiguities.Question Paraphrasing (QP) offers a promising solution as a means to augment existing datasets.The main challenges of current QP models include lack of training data and difficulty in generating diverse and natural questions.In this paper, we present ConQuest, a framework for generating synthetic datasets for contextual question paraphrasing.To this end, ConQuest first employs an answer-aware question generation (QG) model to create a question-pair dataset and then uses this data to train a contextualized question paraphrasing model.We extensively evaluate ConQuest and show its ability to produce more diverse and fluent question pairs than existing approaches.Our contextual paraphrase model also establishes a strong baseline for end-to-end contextual paraphrasing.Further, We find that context can improve BLEU-1 score on contextual compression and expansion by 4.3 and 11.2 respectively, compared to a non-contextual model.
Authors: Mostafa Mirshekari, Jing Gu, Aaron Sisto
Institution: University of California, Santa Cruz
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3213729909