Open Question Answering with Weakly Supervised Embedding Models

Understand

Building computers able to answer questions on any subject is a long standing goal of artificial intelligence.

• Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries.
• Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners.
• In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations.