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natural-language-processing-5

Question-Answer-Generation

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

Yancheng He, Bang Liu, Haolan Chen, Di Niu, Haojie Wei•Sun Jan 26 2020

Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions, dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality.

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MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding

A. Schwing, Mohit Bansal, Avirup Sil, Jaemin Cho, Lifu Huang, Heng Ji, Manling Li, Xudong Lin, Shih-Fu Chang, Revanth Reddy Gangi Reddy, Xilin Rui, Haoyang Wen•Sun Dec 19 2021

A new QA evaluation benchmark with 1,384 questions over news articles that require cross- media grounding of objects in images onto text, and introduces a novel multimedia data augmentation framework, based on cross-media knowledge extraction and synthetic question-answer generation, to automatically augment data that can provide weak supervision for this task.

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Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs

Sung Ju Hwang, Seanie Lee, Dong Bok Lee, Woo Tae Jeong, Donghwan Kim•Thu Apr 30 2020

The Information Maximizing Hierarchical Conditional Variational AutoEncoder (Info-HCVAE) is validated on several benchmark datasets by evaluating the performance of the QA model using only the generated QA pairs (QA-based evaluation) or using both the generated and human-labeled pairs for training, against state-of-the-art baseline models.

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End-to-End Video Question-Answer Generation With Generator-Pretester Network

Winston H. Hsu, Ya-Liang Chang, Hung-Ting Su, Po-Wei Shen, Chen-Hsi Chang, Yu-Siang Wang, Yu-Cheng Chang, Pu-Jen Cheng•Mon Jan 04 2021

A novel model Generator-Pretester Network that focuses on two components: The Joint Question-Answer Generator (JQAG) which generates a question with its corresponding answer to allow Video Question “Answering” training and the Pretester (PT) verifies a generated question by trying to answer it and checks the pretested answer with both the model’s proposed answer and the ground truth answer.

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Quiz-Style Question Generation for News Stories

Cong Yu, Á. Lelkes, Vinh Q. Tran•Wed Feb 17 2021

This work proposes a series of novel techniques for applying large pre-trained Transformer encoder-decoder models, namely PEGASUS and T5, to the tasks of question-answer generation and distractor generation, and shows that these models outperform strong baselines using both automated metrics and human raters.

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TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation

Ramprasaath R. Selvaraju, Mingfei Gao, Chetan Ramaiah, Larry S. Davis, Jun Wang, Yuqian Hu, Ran Xu, J. JáJá•Tue Aug 02 2022

Text-VQA aims at answering questions that require understanding the textual cues in an image. Despite the great progress of existing Text-VQA methods, their performance suffers from insufficient human-labeled question-answer (QA) pairs. However, we observe that, in general, the scene text is not fully exploited in the existing datasets -- only a small portion of the text in each image participates in the annotated QA activities. This results in a huge waste of useful information. To address this deficiency, we develop a new method to generate high-quality and diverse QA pairs by explicitly utilizing the existing rich text available in the scene context of each image. Specifically, we propose, TAG, a text-aware visual question-answer generation architecture that learns to produce meaningful, and accurate QA samples using a multimodal transformer. The architecture exploits underexplored scene text information and enhances scene understanding of Text-VQA models by combining the generated QA pairs with the initial training data. Extensive experimental results on two well-known Text-VQA benchmarks (TextVQA and ST-VQA) demonstrate that our proposed TAG effectively enlarges the training data that helps improve the Text-VQA performance without extra labeling effort. Moreover, our model outperforms state-of-the-art approaches that are pre-trained with extra large-scale data. Code is available at https://github.com/HenryJunW/TAG.

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Question-Answer-Generation | State-of-the-Art