SagDRE: Sequence-Aware Graph-Based Document-Level Relation Extraction with Adaptive Margin Loss
Relation extraction (RE) is an important task for many natural language processing applications. Document-level relation extraction aims to extract the relations within a document and poses many challenges to the RE tasks as it requires reasoning across sentences and handling multiple relations expressed in the same document. Existing state-of-the-art document-level RE models use the graph structure to better connect long-distance correlations. In this work, we propose SagDRE model, which further considers and captures the original sequential information from the text. The proposed model learns sentence-level directional edges to capture the information flow in the document and uses the token-level sequential information to encode the shortest path from one entity to the other. In addition, we propose an adaptive margin loss to maximize the margins to separate positive and negative classes. The experimental results on datasets from various domains demonstrate the effectiveness of our proposed methods.
Code (1)
Tasks
Document-level Relation ExtractionRelationRelation ExtractionSentenceSimilar Papers 제목 키워드 기반
Faithful Abstractive Summarization via Fact-aware Consistency-constrained Transformer
Abstractive summarization is a classic task in Natural Language Generation (NLG), which aims to produce a concise summary of the original document. Recently, great efforts have been made on sequence-to-sequence neural ne…
Abstractive Text SummarizationText GenerationKALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding
With the advent of pretrained language models (LMs), increasing research efforts have been focusing on infusing commonsense and domain-specific knowledge to prepare LMs for downstream tasks. These works attempt to levera…
document understandingKnowledge GraphsLanguage ModellingOpen-Ended Question Answering+1Challenges in Context-Aware Neural Machine Translation
Context-aware neural machine translation involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, and has given rise…
Machine TranslationSentenceTranslationVersionRAG: Version-Aware Retrieval-Augmented Generation for Evolving Documents
Retrieval-Augmented Generation (RAG) systems fail when documents evolve through versioning-a ubiquitous characteristic of technical documentation. Existing approaches achieve only 58-64% accuracy on version-sensitive que…
Intent ClassificationChange DetectionMerge and Recognize: A Geometry and 2D Context Aware Graph Model for Named Entity Recognition from Visual Documents
Named entity recognition (NER) from visual documents, such as invoices, receipts or business cards, is a critical task for visual document understanding. Most classical approaches use a sequence-based model (typically Bi…
document understandingLanguage ModelingLanguage Modellingnamed-entity-recognition+3