AI News, A Neural Network for Factoid Question Answering over Paragraphs

A Neural Network for Factoid Question Answering over Paragraphs

Proceedings of EMNLP 2014, download PDF here UPDATE (10/19/15): A much larger dataset that contains 20,407 questions about 2,347 answers has been released here (31 MB).

Our model is able to succeed where traditional approaches fail, particularly when questions contain very few words (e.g., named entities) indicative of the answer.

for training and evaluating QANTA, along with two datasets1 that each contain around 2,000 question/answer pairs (~10,000 sentences), can be downloaded here (50 MB).

Another interesting observation is that three of the top five answers are authors with well-known unfinished novels, which suggests that QANTA is able to learn information about the answer type without any manually-defined rules.

For example, the blue cluster is predominantly populated by U.S. presidents, and the zoomed plot depicts temporal clustering: presidents who were in office during the same time period occur closer together in the vector space.

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