Explore the Way: Exploring Reasoning Path by Bridging Entities for Effective Cross-Document Relation Extraction (2023)

Abstract: Cross-document relation extraction (CodRED) task aims to infer the relation between two entities mentioned in different documents within a reasoning path. Previous studies have concentrated on merely capturing implicit relations between the entities. However, humans usually utilize explicit information chains such as hyperlinks or additional searches to find the relations between two entities. Inspired by this, we propose Path wIth expLOraTion (PILOT) that provides the enhanced reasoning path by exploring the explicit clue information within the documents. PILOT finds the bridging entities which directly guide the paths between the entities and then employs them as stepstones to navigate desirable paths. We show that models with PILOT outperform the baselines in the CodRED task. Furthermore, we offer a variety of analyses to verify the validity of the reasoning paths constructed through PILOT, including evaluations using large language models such as ChatGPT.

## introduction the relation extraction (re) task aims to predict relations between two entities in a given text and is an essential basis for application tasks such as knowledge base construction (kbc) and question answering (qa) @xcite @xcite . although the sentence-level and document-level re have achieved substantial performance @xcite @xcite , it is still challenging to identify the relations across multiple documents. since the relations between the entities can be inferred from the multiple documents in the wild, cross-document relation extraction (codred) is suggested by @xcite which provides the relation between two entities (i.e., head and tail) across the multiple documents @xcite to implement the cross-document re model, @xcite has enhanced the method of capturing bridging entities by entity-based documentcontext filter and utilizes a cross-document entity relation attention module for enhanced connection awareness between the reasoning paths. on the contrary, @xcite focuses on extracting evidence path and ranking with dense retrieval without explicit consideration for the bridging entity. likewise, previous studies have only attempted to model the bridging entities implicitly, neglecting to exploit the potentially valuable additional information that bridging entities can provide. however, utilizing the explicit information relevant to bridging entities, which can provide a definitive guide in the exploration of reasoning paths, is crucial for identifying the correct relation. when inferring relations between entities using multiple documents, humans may not follow arbitrary in-formation. instead, they instinctively draw upon explicitly linked information from the bridging entity between documents -that which connects a path to the tail entity and provides structural clues about the relation. in this paper, we introduce path with exploration (pilot), an effective way of constructing explicit and structural reasoning path utilizing bridging entities. according to pilot, it first finds the bridging entities with structural entity information, such as intra-document relation, and retrieves the documents that are related to the bridging entities. afterward, we rerank the documents by entity-aware scoring function and leverage the highscored documents as the stepstones connecting the head and tail. the re models with pilot showed consistent improvement in performance compared to the baselines in the codred task. in addition, we provide an analysis with a large language model (llm) such as chatgpt (openai-blog, 2022) on whether our enhanced reasoning path leads to more knowledgeable and relevant information. ## method our proposed method revolves around leveraging the most significant bridging entities to construct a reasoning path between the head and tail entities. initially, we employ structural information (i.e., intra-document relations) and the dense retriever to assemble a set of candidate bridging entities and their corresponding documents. subsequently, we select the most relevant among these candidates through entity-based filtering and scoring methods. finally, we utilize the final bridging entities to construct an expanded reasoning path. ## experiments and analysis we apply pilot to the existing reasoning path provided by @xcite to construct an extended reasoning path. for the experiment, we utilize endto-end @xcite and ecrim @xcite ) models as baselines. the detailed experimental settings are described in 4. ## conclusion we proposed pilot, which is the method that explores the reasoning path by utilizing the bridging entities and their documents for the codred task. based on the filtering and scoring, pilot exploits the explicit and structural information from metadata such as wikidata, and constructs the enhanced reasoning path. in the experiments, the models with pilot showed improvements compared to the baselines and outperformed the existing state-ofthe-art model. moreover, we provided additional experiments and extensive analyses to validate the efficacy of explored reasoning path and analysis.