I Know There Is No Answer: Modeling Answer Validation for Machine Reading Comprehension

2018 ◽  
pp. 85-97 ◽  
Author(s):  
Chuanqi Tan ◽  
Furu Wei ◽  
Qingyu Zhou ◽  
Nan Yang ◽  
Weifeng Lv ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Answer Validation ◽  
Machine Reading

scholarly journals Keyword extraction method for machine reading comprehension based on natural language processing

2021 ◽  
Vol 1955 (1) ◽  
pp. 012072
Author(s):  
Ruiheng Li ◽  
Xuan Zhang ◽  
Chengdong Li ◽  
Zhongju Zheng ◽  
Zihang Zhou ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Extraction Method ◽  
Keyword Extraction ◽  
Machine Reading

scholarly journals Machine Reading Comprehension Framework Based on Self-Training for Domain Adaptation

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 21279-21285
Author(s):  
Hyeon-Gu Lee ◽  
Youngjin Jang ◽  
Harksoo Kim
Keyword(s):  
Reading Comprehension ◽  
Domain Adaptation ◽  
Machine Reading

scholarly journals Towards Reading Comprehension for Long Documents

2018 ◽  
Author(s):  
Yuanxing Zhang ◽  
Yangbin Zhang ◽  
Kaigui Bian ◽  
Xiaoming Li
Keyword(s):  
Reading Comprehension ◽  
Language Comprehension ◽  
Exact Match ◽  
Attention Model ◽  
Comprehension Task ◽  
Proposed Model ◽  
Short Span ◽  
Machine Reading ◽  
The Relationship

Machine reading comprehension has gained attention from both industry and academia. It is a very challenging task that involves various domains such as language comprehension, knowledge inference, summarization, etc. Previous studies mainly focus on reading comprehension on short paragraphs, and these approaches fail to perform well on the documents. In this paper, we propose a hierarchical match attention model to instruct the machine to extract answers from a specific short span of passages for the long document reading comprehension (LDRC) task. The model takes advantages from hierarchical-LSTM to learn the paragraph-level representation, and implements the match mechanism (i.e., quantifying the relationship between two contexts) to find the most appropriate paragraph that includes the hint of answers. Then the task can be decoupled into reading comprehension task for short paragraph, such that the answer can be produced. Experiments on the modified SQuAD dataset show that our proposed model outperforms existing reading comprehension models by at least 20% regarding exact match (EM), F1 and the proportion of identified paragraphs which are exactly the short paragraphs where the original answers locate.

scholarly journals Adversarial Domain Adaptation for Machine Reading Comprehension

2019 ◽  
Author(s):  
Huazheng Wang ◽  
Zhe Gan ◽  
Xiaodong Liu ◽  
Jingjing Liu ◽  
Jianfeng Gao ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Domain Adaptation ◽  
Machine Reading

scholarly journals Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets

2020 ◽  
Vol 34 (05) ◽  
pp. 8918-8927
Author(s):  
Saku Sugawara ◽  
Pontus Stenetorp ◽  
Kentaro Inui ◽  
Akiko Aizawa
Keyword(s):  
Reading Comprehension ◽  
Language Understanding ◽  
Baseline Model ◽  
Complex Reasoning ◽  
Machine Reading

Existing analysis work in machine reading comprehension (MRC) is largely concerned with evaluating the capabilities of systems. However, the capabilities of datasets are not assessed for benchmarking language understanding precisely. We propose a semi-automated, ablation-based methodology for this challenge; By checking whether questions can be solved even after removing features associated with a skill requisite for language understanding, we evaluate to what degree the questions do not require the skill. Experiments on 10 datasets (e.g., CoQA, SQuAD v2.0, and RACE) with a strong baseline model show that, for example, the relative scores of the baseline model provided with content words only and with shuffled sentence words in the context are on average 89.2% and 78.5% of the original scores, respectively. These results suggest that most of the questions already answered correctly by the model do not necessarily require grammatical and complex reasoning. For precise benchmarking, MRC datasets will need to take extra care in their design to ensure that questions can correctly evaluate the intended skills.

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