CAESAR: Context Awareness Enabled Summary-Attentive Reader

Abstract

Comprehending meaning from natural language is a primary objective of Natural Language Processing (NLP), and text comprehension is the cornerstone for achieving this objective upon which all other problems like chat bots, language translation and others can be achieved. We report a Summary-Attentive Reader we designed to better emulate the human reading process, along with a dictiontary-based solution regarding out-of-vocabulary (OOV) words in the data, to generate answer based on machine comprehension of reading passages and question from the SQuAD benchmark. Our implementation of these features with two popular models (Match LSTM and Dynamic Coattention) was able to reach close to matching the results obtained from humans.

References in corpus (9)

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• SQuAD: 100,000+ Questions for Machine Comprehension of Text
• Dynamic Coattention Networks For Question Answering
• Machine Comprehension Using Match-LSTM and Answer Pointer
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• Multi-Perspective Context Matching for Machine Comprehension
• Words or Characters? Fine-grained Gating for Reading Comprehension
• End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension