Open Information Extraction from Question-Answer Pairs

Abstract: Open Information Extraction (OpenIE) extracts meaningful structured tuples from free-form text. Most previous work on OpenIE considers extracting data from one sentence at a time. We describe NeurON, a system for extracting tuples from question-answer pairs. Since real questions and answers often contain precisely the information that users care about, such information is particularly desirable to extend a knowledge base with. NeurON addresses several challenges. First, an answer text is often hard to understand without knowing the question, and second, relevant information can span multiple sentences. To address these, NeurON formulates extraction as a multi-source sequence-to-sequence learning task, wherein it combines distributed representations of a question and an answer to generate knowledge facts. We describe experiments on two real-world datasets that demonstrate that NeurON can find a significant number of new and interesting facts to extend a knowledge base compared to state-of-the-art OpenIE methods.

This document has been adapted from the instructions for earlier ACL and NAACL proceedings, including those for ACL 2018 by Shay Cohen, Kevin Gimpel, and Wei Lu, NAACL 2018 by Margaret Michell and Stephanie Lukin, 2017/2018 (NA)ACL bibtex suggestions from Jason Eisner, ACL 2017 by Dan Gildea and Min-Yen Kan, NAACL 2017 by Margaret Mitchell, ACL 2012 by Maggie Li and Michael White, those from ACL 2010 by Jing-Shing Chang and Philipp Koehn, those for ACL 2008 by JohannaD. Moore, Simone Teufel, James Allan, and Sadaoki Furui, those for ACL 2005 by Hwee Tou Ng and Kemal Oflazer, those for ACL 2002 by Eugene Charniak and Dekang Lin, and earlier ACL and EACL formats. Those versions were written by several people, including John Chen, Henry S. Thompson and Donald Walker. Additional elements were taken from the formatting instructions of the International Joint Conference on Artificial Intelligence and the Conference on Computer Vision and Pattern Recognition.
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Include your own bib file like this: \bibliographystyle{acl_natbib} \begin{thebibliography}{40} 
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Figure and table captions:
Figure 1: Multi-Encoder, Constrained-Decoder model for tuple extraction from (q, a).
Figure 2: State diagram for tag masking rules. V is the vocabulary including placeholder tags, T is the set of placeholder tags.
Table 1: Various types of training instances.
Table 3: Precision (P), Recall (R), and Relative Coverage (RC) results on ConciergeQA.
Table 4: Precision (P), Recall (R), and Relative Coverage (RC) results on AmazonQA dataset.
Figure 3: Example embedding vectors from question and answer encoders. Underlines denote similar embedding vectors in both the encoders.
Table 6: Different errors N and B made by NEURON (N ) and NEURALOPENIE (B) respectively.
Figure 4: Human-in-the-loop system for extending a domain-specific KB.
Figure 5: Screenshot of the instructions and examples of the crowdsourced task.

Questions about this paper:
Where did they get training data?
What extraction model did they use?
Which datasets did they experiment on?

Answers:
AmazonQA and ConciergeQA datasets
Multi-Encoder, Constrained-Decoder model
ConciergeQA and AmazonQA