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A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data
Understanding unstructured text is a ma- jor goal within natural language process- ing. Comprehension tests pose questions based on short text passages to evaluate such understanding. In this work, we …...
Attention-Based Convolutional Neural Network for Machine Comprehension
Understanding open-domain text is one of the primary challenges in NLP. Machine com- prehension benchmarks evaluate a system’s ability to understand text based on the text content only. In this …...
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability
AbstractKnowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: …...
Reading Comprehension with Graph-based Temporal-Casual Reasoning
AbstractComplex questions in reading comprehension tasks require integrating information from multiple sentences. In this work, to answer such questions involving temporal and causal relations, we generate event graphs from text …...
Supervised and Unsupervised Transfer Learning for Question Answering
AbstractAlthough transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied. In this paper, …...
Improving Machine Reading Comprehension with General Reading Strategies
AbstractReading strategies have been shown to improve comprehension levels, especially for readers lacking adequate prior knowledge. Just as the process of knowledge accumulation is time-consuming for human readers, it is …...
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text
AbstractThis paper focuses on the topic of inferential machine comprehension, which aims to fully understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills. …...