C ompact IE : Compact Facts in Open Information Extraction (2022)

Abstract: A major drawback of modern neural OpenIE systems and benchmarks is that they prioritize high coverage of information in extractions over compactness of their constituents. This severely limits the usefulness of OpenIE extractions in many downstream tasks. The utility of extractions can be improved if extractions are compact and share constituents. To this end, we study the problem of identifying compact extractions with neural-based methods. We propose CompactIE, an OpenIE system that uses a novel pipelined approach to produce compact extractions with overlapping constituents. It first detects constituents of the extractions and then links them to build extractions. We train our system on compact extractions obtained by processing existing benchmarks. Our experiments on CaRB and Wire57 datasets indicate that CompactIE finds 1.5x-2x more compact extractions than previous systems, with high precision, establishing a new state-of-the-art performance in OpenIE.

## introduction a popular domain-agnostic paradigm to structure the raw text is open information extraction (ope-nie) @xcite . not relying on any predefined schema, openie systems typically extract information as (subject; relation; object) triples. the extracted information is then used in several downstream applications, including answering questions @xcite , summarizing documents @xcite , and populating knowledge bases @xcite . despite much progress, state-of-the-art neural openie systems focus on covering more information from the input sentence often at the cost of utility and compactness of the extracted triples. the extracted triples have long, over-specific constituents (i.e. the relation and its arguments). figure 1 illustrates such example triples produced by a popular beth is the second child of henry, born in wedlock. ## background and preliminaries given a sentence @xmath0...w n , an openie system generates triples of the form (subject; relation; object), where subject, relation and object are the constituents of a triple. ## approach in this section we describe our pipeline system, compactie. we first detail the constituent extraction model, its training constraints, and the decoding algorithm in section 3.1. then, we describe the constituent linking model in section 3.2. figure 3 shows an overview of compactie architecture. ## benchmark creation to train the constituent extraction and constituent linking models for extracting compact triples, we need a benchmark of compact triples. existing openie benchmark foot_1 is created by combining extractions from multiple existing openie systems. although widely adopted, we observed that it includes over-specific and sometimes incorrect extractions from previous systems. this encouraged us to design a data processing algorithm that can extract compact triples from scratch. inspired by rule-based openie system @xcite , we find compact triples by extracting the following clauses within a sentence: main clauses are independent clauses that express a complete concept. complement clauses are subordinate clauses that serve to complete the meaning of a verb or noun in the sentence. coordinate clauses are independent clauses joined to the main clause using coordinating conjunctions such as and, or, but, etc. we identify clauses within a sentence using its dependency graph. we first build a sentence tree such that the root is the head of the main clauses and the first-level children are clauses modifying the root word. we then perform a postfix traversal of the tree until we find a sub-tree with no clausal children. at this point, we run a standard ope-nie system, imojie @xcite , over the clause corresponding to the sub-tree to obtain triples. we then backtrack and extract triples for other clausal children and lastly the parent. we provide pseudo-code of algorithm in appendix a. we run our algorithm on each multi-clause sentence in the openie2016 benchmark and obtain a new benchmark tailored for extracting compact triples. figure 1 shows example sentences and compact triples from this benchmark. ## experimental setup training dataset: we train compactie using the benchmark described in section 4. ## related work openie has been studied extensively for over a decade with a history of statistical and rule-based systems @xcite @xcite @xcite that extract triples from sentences without using any training data. recently, neural models have been developed that are trained endto-end on extractions bootstrapped from previous openie systems. these can broadly be classified into labeling-based and generation-based systems. labeling-based systems @xcite @xcite tag each word in the sentence and construct triples in an autoregressive manner or by using a unique predicate for each triple. generation-based systems @xcite use a sequenceto-sequence model to generate triples one word at a time. labeling-based systems can handle redundancy in extracted triples and are faster than generation-based systems @xcite . ## conclusion in this work we extract compact triples from single sentences using an end-to-end pipelined approach, first extracting triple constituents using a novel table filling model and then determining relations between them with a classifier. our method achieves excellent performance in producing exhaustive compact triples with high precision. we hope that compactie serves as a strong baseline and makes us re-think the value of all-at-once information extraction systems.