Question Answering

Опубликовано 22 июня 2016, 2:40

Natural-language question answering (QA) has clear practical and scientific values, such as evaluating a machine’s understanding of a domain, or providing succinct and precise answers to search engine queries. While both Bing and Google have incorporated more “semantics” to return direct answers to queries, QA is far from solved, and is becoming more important as natural language interaction becomes popular (e.g., Siri and Cortana). In this session, we invite experts from both academia and Microsoft to present recent technologies for improving QA. The topics include traditional IR approaches for QA, machine reading for knowledge acquisition and representation, and semantic parsing for answering questions using structured databases like Freebase or Satori. In addition, we invite product groups (Bing QnA team) to discuss the technical challenges faced in the real-world scenarios and highlight the research need.