Explicit Utilization of General Knowledge in Machine Reading Comprehension

By Chao Wang, Hui Jiang. 2018

42 citations recorded by the source corpus.

To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper, we explore how to integrate the neural networks of MRC models with the general knowledge of human beings. On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. On the other hand, we propose an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms. Based on the data enrichment method, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them. When only a subset (20%-80%) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise.

Subfields: Natural Language Processing

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Citations in the corpus

Knowledge Enhanced Contextual Word Representations (2019, background): “Task-specific KB architectures Other work has focused on integrating KBs into neural architectures for specific downstream tasks (Yang and Mitchell, 2017; Sun et al., 2018; Chen et al., 2018; Bauer et al., 2018; Mihaylov and Frank, 2018; Wang and Jiang, 2019; Yang et al., 2019).”
Unsupervised Commonsense Question Answering with Self-Talk (2020, background): “Additional resources include WordNet (Lin et al., 2017; Wang and Jiang, 2019), mining scripts from corpora (Lin et al.”
A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving (2020, background): “Recently, many studies have used deep learning methods to incorporate external knowledge from the knowledge base into many NLP tasks, such as dialogue systems (Zhong et al., 2019) and reading comprehension (Wang and Jiang, 2019; Qiu et al., 2019).”
Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering (2020, background): “Other models such as in the work of (Mihaylov and Frank, 2018; Chen et al., 2018; Yang et al., 2019; Wang and Jiang, 2019) embed syntactic or semantic knowledge into knowledge enriched embeddings.”
How Additional Knowledge can Improve Natural Language Commonsense Question Answering (2019, background): “External Knowledge: Models that integrate ex-ternal knowledge have been introduced in (Mi-haylov and Frank, 2018; Chen et al., 2018; Yang et al., 2019; Wang and Jiang, 2019).”