Improving Unsupervised Question Answering via Summarization-Informed Question Generation
Citation:
Lyu, Chenyang and Shang, Lifeng and Graham, Yvette and Foster, Jennifer and Jiang, Xin and Liu, Qun, Improving Unsupervised Question Answering via Summarization-Informed Question Generation, 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, Association for Computational Linguistics, 2021, 4134 - 4148Download Item:
Abstract:
Question Generation (QG) is the task of generating a plausible question for a given <passage, answer> pair. Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to train a system to generate a question given a passage and an answer. A disadvantage of the heuristic approach is that the generated questions are heavily tied to their declarative counterparts. A disadvantage of the supervised approach is that they are heavily tied to the domain/language of the QA dataset used as training data. In order to overcome these shortcomings, we propose an unsupervised QG method which uses questions generated heuristically from summaries as a source of training data for a QG system. We make use of freely available news summary data, transforming declarative summary sentences into appropriate questions using heuristics informed by dependency parsing, named entity recognition and semantic role labeling. The resulting questions are then
combined with the original news articles to train an end-to-end neural QG model. We extrinsically evaluate our approach using unsupervised QA: our QG model is used to generate synthetic QA pairs for training a QA model. Experimental results show that, trained with only 20k English Wikipedia-based synthetic QA pairs, the QA model substantially outperforms previous unsupervised models on three in-domain datasets (SQuAD1.1, Natural Questions, TriviaQA) and three out-of-domain datasets (NewsQA, BioASQ, DuoRC), demonstrating the transferability of the approach.
Author's Homepage:
http://people.tcd.ie/ygrahamDescription:
PUBLISHEDOnline and Punta Cana, Dominican Republic
Author: Graham, Yvette
Other Titles:
2021 Conference on Empirical Methods in Natural Language ProcessingPublisher:
Association for Computational LinguisticsType of material:
Conference PaperCollections
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Full text availableSubject (TCD):
Digital Engagement , Information Technology , Natural Language ProcessingMetadata
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