CAESAR: Context Awareness Enabled Summary-Attentive Reader
This work addresses text comprehension for NLP applications like chatbots and translation, but it is incremental as it builds on existing models.
The authors tackled machine reading comprehension by designing a Summary-Attentive Reader with a dictionary-based solution for out-of-vocabulary words, achieving results close to human performance on the SQuAD benchmark.
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.