Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision
Distant supervision (DS) is an important paradigm for automatically extracting relations. It utilizes existing knowledge base to collect examples for the relation we intend to extract, and then uses these examples to automatically generate the training data. However, the examples collected can be very noisy, and pose significant challenge for obtaining high quality labels. Previous work has made remarkable progress in predicting the relation from distant supervision, but typically ignores the temporal relations among those supervising instances. This paper formulates the problem of relation extraction with temporal reasoning and proposes a solution to predict whether two given entities participate in a relation at a given time spot. For this purpose, we construct a dataset called WIKI-TIME which additionally includes the valid period of a certain relation of two entities in the knowledge base. We propose a novel neural model to incorporate both the temporal information encoding and sequential reasoning. The experimental results show that, compared with the best of existing models, our model achieves better performance in both WIKI-TIME dataset and the well-studied NYT-10 dataset.
Code (1)
Tasks
RelationRelation ExtractionvalidSimilar Papers 제목 키워드 기반
Distributed Associative Memory Network with Association Reinforcing Loss
Despite recent progress in memory augmented neural network research, associative memory networks with a single external memory still show limited performance on complex relational reasoning tasks. The main reason for thi…
MemorizationRelational ReasoningMAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents
Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on semantic similarity over monolithic memory stores, entanglin…
Semantic SimilarityMemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models
Memory is essential for large vision-language models (LVLMs) to handle long, multimodal interactions, with two method directions providing this capability: long-context LVLMs and memory-augmented agents. However, no exis…
Information ExtractionVisual GroundingRAVU: Retrieval Augmented Video Understanding with Compositional Reasoning over Graph
Comprehending long videos remains a significant challenge for Large Multi-modal Models (LMMs). Current LMMs struggle to process even minutes to hours videos due to their lack of explicit memory and retrieval mechanisms. …
EgoSchemaRetrievalVideo UnderstandingTA-Mem: Tool-Augmented Autonomous Memory Retrieval for LLM in Long-Term Conversational QA
Large Language Model (LLM) has exhibited strong reasoning ability in text-based contexts across various domains, yet the limitation of context window poses challenges for the model on long-range inference tasks and neces…