MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization

Xu, Canwen; Pei, Jiaxin; Wu, Hongtao; Liu, Yi-Yu; Li, Chenliang

doi:10.18653/v1/2020.acl-main.330

Cited by 11 publications

(12 citation statements)

References 44 publications

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“…Yadav et al (2021a) proposed question-aware transformer models for question summarization. Xu et al (2020) automatically created a Chinese dataset (MATINF) for medical question answering, summarization, and classification tasks focusing on maternity and infant categories. Some of the other prominent works in the abstractive summarization of long and short documents include Cohan et al (2018); Zhang et al (2019a); MacAvaney et al (2019); Sotudeh et al (2020).…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

Yadav¹,

Gupta²,

Abacha³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems. A recent study showed that manual summarization of consumer health questions brings significant improvement in retrieving relevant answers. However, the automatic summarization of long questions is a challenging task due to the lack of training data and the complexity of the related subtasks, such as the question focus and type recognition. In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization. We propose two novel rewards obtained from the downstream tasks of (i) question-type identification and (ii) question-focus recognition to regularize the question generation model. These rewards ensure the generation of semantically valid questions and encourage the inclusion of key medical entities/foci in the question summary. We evaluated our proposed method on two benchmark datasets and achieved higher performance over state-of-theart models. The manual evaluation of the summaries reveals that the generated questions are more diverse and have fewer factual inconsistencies than the baseline summaries.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

Yadav¹,

Gupta²,

Abacha³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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“…For ProphetNet-En, we reports the results on summarization tasks CNN/DM (Hermann et al, 2015), Gigaword (Rush et al, 2015), and MSNews (Liu et al, 2020a); question generation tasks SQuAD 1.1 (Rajpurkar et al, 2016) and MSQG (Liu et al, 2020a). (Vinyals and Le, 2015) 0.336 0.238 0.03 0.128 0.183 0.448 0.353 0.004 0.016 0.205 iVAE MI (Fang et al, 2019) 0 Table 6: Results of ProphetNet-Dialog-En on DailyDialog and PersonaChat.…”

Section: Finetuning Benchmarksmentioning

confidence: 99%

“…We provide six pre-trained models with downstream task finetuning scripts, including ProphetNet-En pre-trained with 160GB English raw text, ProphetNet-Zh pre-trained with 160GB Chinese raw text, ProphetNet-Multi with 101GB Wiki-100 corpus and 1.5TB Common Crawl 2 data, ProphetNet-Dialog-En with 60 million sessions Reddit open-domain dialog corpus, ProphetNet-Dialog-Zh with collected Chinese dialog corpus over 30 million sessions, and ProphetNet-Code pre-trained with 10 million codes and documents. ProphetNet-X achieves new state-of-the-art results on 10 benchmarks, including Chinese summarization (MATINF-SUMM (Xu et al, 2020a) and LC-STS (Hu et al, 2015)), Chinese question answering (MATINF-QA (Xu et al, 2020a)), cross-lingual generation (XGLUE NTG (Liang et al, 2020) and Figure 1: A diagram of ProphetNet-X framework. ProphetNet-X models share the same model structure and cover various languages and domains.…”

Section: Introductionmentioning

confidence: 99%

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation

Qi¹,

Gong²,

Yu³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Now, the pre-training technique is ubiquitous in natural language processing field. Prophet-Net is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks. In this paper, we extend ProphetNet into other domains and languages, and present the ProphetNet family pretraining models, named ProphetNet-X, where X can be English, Chinese, Multi-lingual, and so on. We pre-train a cross-lingual generation model ProphetNet-Multi, a Chinese generation model ProphetNet-Zh, two open-domain dialog generation models ProphetNet-Dialog-En and ProphetNet-Dialog-Zh. And also, we provide a PLG (Programming Language Generation) model ProphetNet-Code to show the generation performance besides NLG (Natural Language Generation) tasks. In our experiments, ProphetNet-X models achieve new state-of-the-art performance on 10 benchmarks. All the models of ProphetNet-X share the same model structure, which allows users to easily switch between different models. We make the code and models publicly available 1 , and we will keep updating more pre-training models and finetuning scripts.

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“…Previous studies (Dergousoff and Mandryk, 2015;van Berkel et al, 2017) reveal that gamification can often improve the quality of collected data. Instead of collecting data from the wild like most datasets (Zhang et al, 2014(Zhang et al, , 2015Xu et al, 2020b), we collect the data from historical game records of Decrypto Online, a well-designed online board game. The screenshot of the user interface is shown in Figure 2.…”

Section: Data Collectionmentioning

confidence: 99%

Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge

Xu¹,

Zhou²,

Ge³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

Self Cite

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Cant is important for understanding advertising, comedies and dog-whistle politics. However, computational research on cant is hindered by a lack of available datasets. In this paper, we propose a large and diverse Chinese dataset for creating and understanding cant from a computational linguistics perspective. We formulate a task for cant understanding and provide both quantitative and qualitative analysis for tested word embedding similarity and pretrained language models. Experiments suggest that such a task requires deep language understanding, common sense, and world knowledge and thus can be a good testbed for pretrained language models and help models perform better on other tasks. 1

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MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization

Cited by 11 publications

References 44 publications

Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation

Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge

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