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:10.48550/arxiv.2004.12302

Cited by 3 publications

(7 citation statements)

References 39 publications

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“…TextRank (Mihalcea and Tarau, 2004) and LexRank (Erkan and Radev, 2004) are extractive baselines and others are abstractive baselines. MTF-S2S single (Xu et al, 2020a) and MTF-S2S multi denote single task finetuning and multi-task finetuning on MATINF dataset. We see consistent gains on both Chinese question answering task and summarization tasks.…”

Section: Resultsmentioning

confidence: 99%

“…For ProphetNet-En, we report the results for ProphetNet in Table 10 and Table 11. We also report the results for two new tasks MSNTG and MSQG introduced from GLGE (Liu et al, 2020a).…”

Section: Resultsmentioning

confidence: 99%

“…For ProphetNet-Zh, we evaluate our pre-trained model with MATINF-QA (Xu et al, 2020a) for generative question answering task, MATINF-SUMM (Xu et al, 2020a) and LCSTS (Hu et al, 2015) for summarization task.…”

Section: Finetuning Benchmarksmentioning

confidence: 99%

“…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).…”

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 3 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 XGLUE QG (Liang et al, 2020)), English summarization (MSNews (Liu et al, 2020a)), English dialog generation (DailyDialog (Li et al, 2017), PersonaChat (Zhang et al, 2018), and DSTC7-AVSD (Alamri et al, 2019)), and code summarization (CodeXGLUE (Lu et al, 2021)). Users can simply download the ProphetNet-X repository and find corresponding pre-trained model with downstream task finetuning scripts.…”

Section: Introductionmentioning

confidence: 99%

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

Gong

et al. 2021

Preprint

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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. A video 2 to introduce ProphetNet-X usage is also released.

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Section: Resultsmentioning

confidence: 99%

“…For ProphetNet-En, we report the results for ProphetNet in Table 10 and Table 11. We also report the results for two new tasks MSNTG and MSQG introduced from GLGE (Liu et al, 2020a).…”

Section: Resultsmentioning

confidence: 99%

Section: Finetuning Benchmarksmentioning

confidence: 99%

Section: Finetuning Benchmarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Gong

et al. 2021

Preprint

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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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Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond

Zhang,

Zhao,

Wang

2020

Preprint

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Machine reading comprehension (MRC) aims to teach machines to read and comprehend human languages, which is a long-standing goal of natural language processing (NLP). With the burst of deep neural networks and the evolution of contextualized language models (CLMs), the research of MRC has experienced two significant breakthroughs. MRC and CLM, as a phenomenon, have a great impact on the NLP community. In this survey, we provide a comprehensive and comparative review on MRC covering overall research topics about 1) the origin and development of MRC and CLM, with particular focus on the role of CLMs; 2) the impact of MRC and CLM to the NLP community; 3) the definition, datasets, and evaluation of MRC; 4) general MRC architecture and technical methods in the view of two-stage Encoder-Decoder solving architecture from the insights of the cognitive process of humans; 5) previous highlights, emerging topics, and our empirical analysis, among which we especially focus on what works in different periods of MRC researches. We propose a full-view categorization and new taxonomies on these topics. The primary views we have arrived at are that 1) MRC boosts the progress from language processing to understanding; 2) the rapid improvement of MRC systems greatly benefits from the development of CLMs; 3) the theme of MRC is gradually moving from shallow text matching to cognitive reasoning.

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

Cited by 3 publications

References 39 publications

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

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

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

Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond

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