Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study

Open-domain textual question answering techniques

Textual question answering is a technique of extracting a sentence or text snippet from a document or document collection that responds directly to a query. Open-domain textual question answering presupposes that questions are natural and unrestricted with respect to topic. The question answering (Q/A) techniques, as embodied in today’s systems, can be roughly divided into two types: (1) techni...

A Neural Comprehensive Ranker (NCR) for Open-Domain Question Answering

This paper proposes a novel neural machine reading model for open-domain question answering at scale. Existing machine comprehension models typically assume that a short piece of relevant text containing answers is already identified and given to the models, from which the models are designed to extract answers. This assumption, however, is not realistic for building a large-scale open-domain q...

A Comprehensive Resource to Evaluate Complex Open Domain Question Answering

We describe two corpora of question and answer pairs collected for complex, open-domain Question Answering (QA) to enable answer classification and re-ranking experiments. We deliver manually annotated answers to non-factoid questions from a QA system on both Web and TREC data. Moreover, we provide the same question/answer pairs in a rich data representation that includes syntactic parse trees ...

Open-domain question answering

Question answering aims to develop techniques that can go beyond the retrieval of relevant documents in order to return exact answers to natural language questions, such as “How tall is the Eiffel Tower?”, “Which cities have a subway system?”, and “Who is Alberto Tomba?”. Answering natural language questions requires more complex processing of text than employed by current information retrieval...

Open-Domain Question-Answering