Abstractive Open Information Extraction

Kevin Song Pei, Ishan Jindal, Kevin Chang

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Keywords: Open Information Extraction, Relation Extraction, Natural Language Generation

TL;DR: We introduce abstractive Open Information Extraction to extract relations without needing to use the tokens from the original sentence, in contrast to preexisting extractive Open Information models.

Abstract:

Open Information Extraction (OpenIE) is a traditional NLP task that extracts structured information from unstructured text to be used for other downstream applications. Traditionally, OpenIE focuses on extracting the surface forms of relations as they appear in the raw text, which we term extractive OpenIE. One of the main drawbacks of this approach is that implicit semantic relations (inferred relations) can not be extracted, compromising the performance of downstream applications. In this paper, we broaden the scope of OpenIE relations from merely the surface form of relations to include inferred relations, which we term abstractive OpenIE. This new task calls for the development of a new abstractive OpenIE training dataset and a baseline neural model that can extract those inferred relations. We also demonstrate the necessity for a new semantics-based metric for evaluating abstractive OpenIE extractions. Via a case study on Complex QA, we demonstrate the effectiveness of abstractive OpenIE.

Submission Number: 4711