Results 41 to 50 of about 335 (211)

NumNet: Machine Reading Comprehension with Numerical Reasoning [PDF]

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019
Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human's reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) systems. To address this issue, we propose a numerical MRC model named as NumNet, which utilizes a numerically-aware graph neural network to ...
Qiu Ran, Yankai Lin 0001, Peng Li 0030, Jie Zhou 0016, Zhiyuan Liu 0001   +4 more
openaire   +3 more sources

DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension

Transactions of the Association for Computational Linguistics, 2019
We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Collected from English as a Foreign Language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our data ...
Sun, Kai, Yu, Dian, Chen, Jianshu, Yu, Dong, Choi, Yejin, Cardie, Claire   +5 more
doaj   +1 more source

Multilingual multi-aspect explainability analyses on machine reading comprehension models

iScience, 2022
Summary: Achieving human-level performance on some of the machine reading comprehension (MRC) datasets is no longer challenging with the help of powerful pre-trained language models (PLMs).
Yiming Cui, Wei-Nan Zhang, Wanxiang Che, Ting Liu, Zhigang Chen, Shijin Wang   +5 more
doaj   +1 more source

Cooperative Self-training of Machine Reading Comprehension

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022
Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings. However, training question answering models still requires large amounts of annotated data for specific domains.
Hongyin Luo, Shang-Wen Li 0001, Mingye Gao, Seunghak Yu, James R. Glass   +4 more
openaire   +3 more sources

Scene Restoring for Narrative Machine Reading Comprehension [PDF]

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
This paper focuses on machine reading comprehension for narrative passages. Narrative passages usually describe a chain of events. When reading this kind of passage, humans tend to restore a scene according to the text with their prior knowledge, which helps them understand the passage comprehensively.
Zhixing Tian, Yuanzhe Zhang, Kang Liu 0001, Jun Zhao 0001, Yantao Jia, Zhicheng Sheng   +5 more
openaire   +1 more source

Machine Reading Comprehension: a Literature Review

CoRR, 2019
Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence. This article summarizes recent advances in MRC, mainly focusing on two aspects (i.e., corpus and techniques). The specific characteristics of various MRC corpus are listed and compared.
Xin Zhang, Yang An, Sujian Li, Yizhong Wang   +3 more
openaire   +3 more sources

Continual Domain Adaptation for Machine Reading Comprehension [PDF]

Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020
Machine reading comprehension (MRC) has become a core component in a variety of natural language processing (NLP) applications such as question answering and dialogue systems. It becomes a practical challenge that an MRC model needs to learn in non-stationary environments, in which the underlying data distribution changes over time.
Lixin Su, Jiafeng Guo, Ruqing Zhang 0001, Yixing Fan, Yanyan Lan, Xueqi Cheng   +5 more
openaire   +3 more sources

Improving Machine Reading Comprehension with Multi-Task Learning and Self-Training

Mathematics, 2022
Machine Reading Comprehension (MRC) is an AI challenge that requires machines to determine the correct answer to a question based on a given passage, in which extractive MRC requires extracting an answer span to a question from a given passage, such as ...
Jianquan Ouyang, Mengen Fu
doaj   +1 more source

Mixed inference machine reading comprehension method based on symbolic logic

Intelligent Systems with Applications
With the rapid development of machine learning, challenging question and answer datasets have also emerged, and the machine reading comprehension technology has emerged.
Duanduan Liu
doaj   +1 more source

Machine Reading Comprehension Model Based on MacBERT and Adversarial Training [PDF]

Jisuanji gongcheng
Machine reading comprehension is designed to allow machines to understand natural language texts, resembling humans, and perform question-answering tasks accordingly.
ZHOU Zhaochen, FANG Qingmao, WU Xiaohong, HU Ping, HE Xiaohai
doaj   +1 more source