scholarly journals Adversarial Training for Machine Reading Comprehension with Virtual Embeddings

2021 ◽  
Author(s):  
Ziqing Yang ◽  
Yiming Cui ◽  
Chenglei Si ◽  
Wanxiang Che ◽  
Ting Liu ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Adversarial Training ◽  
Machine Reading

scholarly journals Multi-Task Learning with Generative Adversarial Training for Multi-Passage Machine Reading Comprehension

2020 ◽  
Vol 34 (05) ◽  
pp. 8705-8712
Author(s):  
Qiyu Ren ◽  
Xiang Cheng ◽  
Sen Su
Keyword(s):  
Reading Comprehension ◽  
Open Domain ◽  
Training Process ◽  
Task Learning ◽  
Novel Approach ◽  
Adversarial Training ◽  
Model Training ◽  
Final Answer ◽  
Machine Reading ◽  
Candidate Set

Multi-passage machine reading comprehension (MRC) aims to answer a question by multiple passages. Existing multi-passage MRC approaches have shown that employing passages with and without golden answers (i.e. labeled and unlabeled passages) for model training can improve prediction accuracy. In this paper, we present MG-MRC, a novel approach for multi-passage MRC via multi-task learning with generative adversarial training. MG-MRC adopts the extract-then-select framework, where an extractor is first used to predict answer candidates, then a selector is used to choose the final answer. In MG-MRC, we adopt multi-task learning to train the extractor by using both labeled and unlabeled passages. In particular, we use labeled passages to train the extractor by supervised learning, while using unlabeled passages to train the extractor by generative adversarial training, where the extractor is regarded as the generator and a discriminator is introduced to evaluate the generated answer candidates. Moreover, to train the extractor by backpropagation in the generative adversarial training process, we propose a hybrid method which combines boundary-based and content-based extracting methods to produce the answer candidate set and its representation. The experimental results on three open-domain QA datasets confirm the effectiveness of our approach.

scholarly journals A Robust Adversarial Training Approach to Machine Reading Comprehension

2020 ◽  
Vol 34 (05) ◽  
pp. 8392-8400 ◽  
Author(s):  
Kai Liu ◽  
Xin Liu ◽  
An Yang ◽  
Jing Liu ◽  
Jinsong Su ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
State Of The Art ◽  
The State ◽  
Training Data ◽  
Training Dataset ◽  
Original Dataset ◽  
Training Approach ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
Machine Reading

Lacking robustness is a serious problem for Machine Reading Comprehension (MRC) models. To alleviate this problem, one of the most promising ways is to augment the training dataset with sophisticated designed adversarial examples. Generally, those examples are created by rules according to the observed patterns of successful adversarial attacks. Since the types of adversarial examples are innumerable, it is not adequate to manually design and enrich training data to defend against all types of adversarial attacks. In this paper, we propose a novel robust adversarial training approach to improve the robustness of MRC models in a more generic way. Given an MRC model well-trained on the original dataset, our approach dynamically generates adversarial examples based on the parameters of current model and further trains the model by using the generated examples in an iterative schedule. When applied to the state-of-the-art MRC models, including QANET, BERT and ERNIE2.0, our approach obtains significant and comprehensive improvements on 5 adversarial datasets constructed in different ways, without sacrificing the performance on the original SQuAD development set. Moreover, when coupled with other data augmentation strategy, our approach further boosts the overall performance on adversarial datasets and outperforms the state-of-the-art methods.

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.

Sign in / Sign up

Export Citation Format

Share Document