Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems (2022)

Abstract: Information extraction systems analyze text to produce entities and beliefs, but their output often has errors. In this paper, we analyze the reading consistency of the extracted facts with respect to the text from which they were derived and show how to detect and correct errors. We consider both the scenario when the provenance text is automatically found by an information extraction system and when it is curated by humans. We contrast consistency with credibility ; define and explore consistency and repair tasks; and demonstrate a simple yet effective and generalizable model. We analyze these tasks and evaluate this approach on three datasets. Against a strong baseline model, we consistently improve both consistency and repair across three datasets using a simple MLP model with attention and lexical features.

## introduction information extraction (ie) systems read text to extract entities, and relations and create beliefs represented in a knowledge graph. current systems though are far from perfect: e.g., in the 2017 text analysis conference (tac) knowledge base population task, participants created knowledge graphs with relations like cause of death and city of headquarters from news corpora @xcite . when manually evaluated, no system had achieved an f1 score above 0.3 @xcite . one reason for such low scores is inconsistency between the text and the extracted beliefs. we consider a belief to be consistent if the text from which it was extracted linguistically supports it (regardless of any logical or real-world factual truth). we show the difference between consistent and inconsistent readings, along with a potential correction, in fig. 1 . in fig. 1a , the system considered harry reid was charged with an assault, which is not consistent with the provenance sentence. in fig. 1b the system is consistent in constructing its belief. ## approach our approach computes both the consistency of a belief b i and a "repaired" belief with respect to a given set of provenance sentences. we represent b i as a triple ⟨subject i , predicate i , object i ⟩ and the set of provenance sentences as s i,1 , s i,2 , ...s i,n . the system outputs two discrete predictions: (1) a binary one indicating whether the belief is consistent with the sentences, and (2) a categorical one sug-figure 3 : given a belief and a set of n provenance sentences, our framework determines its consistency and suggests a repair when if is deemed inconsistent. our approach has three main modules: representation (4.1), combination (4.2), and feature learning and classification (4.3). gesting a repair. fig. 3 illustrates our approach for representing and combining the beliefs and provenance sentences to jointly learn the two tasks. our approach has three main steps: embedding a belief and its provenance sentences in a vector space ( §4.1), combining/aggregating these representations ( §4.2), and using the result for additional feature learning and classification ( §4.3). we describe our loss objective in §4.4. as we show, our framework can be thought of as generalizing high performing credibility models, such as declare @xcite or lstm-text (rashkin et al., 2017). ## experiments we centered our study around four questions, answered throughout §5.3. (1) as our approach subsumes credibility models, can those credibility models also be used for the consistency and/or repair tasks ( §5.3.1)? (2) what features and representations are important for the consistency and repair tasks ( §5.3.2)? (3) how important is it to model the realized (sequential) order of words within the provenance sentences for our tasks ( §5.3.3)? (4) what are the differences between relation repair and extraction ( §5.3.4)? ## related studies there has been research on determining the consistency of beliefs using either schemas or ensembles, but none that are language-based, do not require access to ie system details, or attempt to repair inconsistent facts. our work addresses all these. schema and ensemble based approaches: previous work by @xcite and @xcite determined the consistency of the extracted belief using a schema as the side information and coupling constraints to satisfy the schema's axioms. rather than applying schemas, @xcite proposed an unsupervised method applying linguistic features to filter credible vs. non-credible belief. however, it required access to multiple ie systems with different configuration settings that extracted information from the same text corpus. @xcite used a supervised approach to build a classifier from the confidence scores produced by multiple ie systems for the same belief. these are not standalone systems, as they assume the availability of multiple ie systems. language based approaches: the fever @xcite fact-checking study proposes a framework for credibility task and performs provenance-based classification without attempting to repair errors. this task has inspired a number of efforts @xcite , including @xcite who tackle a problem similar to our consistency. @xcite outlines additional language-based approaches for consistency prediction (they term it "verdict prediction"). however, a crucial difference is that we aim to operate on kg tuple outputs as the belief (not sentences). overall, our study differs from previous ones in two important ways. (1) we address the problem of determining consistency and potential corrections without access to an underlying semantic schema. ## conclusions we propose a task of refining the beliefs produced by a blackbox ie system that provides no access to or knowledge of its internal workings. first we analyze the types of errors made. then we propose two subtasks: determining the consistency of an extracted belief and its provenance text, and suggesting a repair to fix the belief. we present a modular framework that can use a variety of representation, and learning techniques, and subsumes prior work. this framework provides effective techniques for the consistency and repair tasks.