Abstract: The relationship between two entities in a sentence is often implied by word order and common sense, rather than an explicit predicate. For example, it is evident that"Fed chair Powell indicates rate hike"implies (Powell, is a, Fed chair) and (Powell, works for, Fed). These tuples are just as significant as the explicit-predicate tuple (Powell, indicates, rate hike), but have much lower recall under traditional Open Information Extraction (OpenIE) systems. Implicit tuples are our term for this type of extraction where the relation is not present in the input sentence. There is very little OpenIE training data available relative to other NLP tasks and none focused on implicit relations. We develop an open source, parse-based tool for converting large reading comprehension datasets to OpenIE datasets and release a dataset 35x larger than previously available by sentence count. A baseline neural model trained on this data outperforms previous methods on the implicit extraction task.

Introduction: Open Information Extraction (OpenIE) is the NLP task of generating (subject, relation, object) tuples from unstructured text e.g. “Fed chair Powell indicates rate hike” outputs (Powell, indicates, rate hike). The modifier open is used to contrast IE research in which the relation belongs to a fixed set. OpenIE has been shown to be useful for several downstream applications such as knowledge base construction BIBREF0 , textual entailment BIBREF1 , and other natural language understanding tasks BIBREF2 . In our previous example an extraction was missing: (Powell, works for, Fed). Implicit extractions are our term for this type of tuple where the relation (“works for” in this example) is not contained in the input sentence. In both colloquial and formal language, many relations are evident without being explicitly stated. However, despite their pervasiveness, there has not been prior work targeted at implicit predicates in the general case. Implicit information extractors for some specific implicit relations such as noun-mediated relations, numerical relations, and others BIBREF3 , BIBREF4 , BIBREF5 have been researched. While specific extractors are important, there are a multiplicity of implicit relation types and it would be intractable to categorize and design extractors for each one. Past general OpenIE systems have been plagued by low recall on implicit relations BIBREF6 . In OpenIE's original application – web-scale knowledge base construction – this low recall is tolerable because facts are often restated in many ways BIBREF7 . However, in downstream NLU applications an implied relationship may be significant and only stated once BIBREF2 . The contribution of this work is twofold. In Section 4, we introduce our parse-based conversion tool and convert two large reading comprehension datasets into implicit OpenIE datasets. In Section 5 and 6, we train a simple neural model on this data and compare to previous systems on precision-recall curves using a new gold test set for implicit tuples.

Question: How much better does this baseline neural model do?
Answer: The model outperforms at every point in the
implicit-tuples PR curve reaching almost 0.8 in recall