Learning Contextualized Knowledge Structures for Commonsense Reasoning
Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks. However, KG edge (fact) sparsity and noisy edge extraction/generation often hinder models from obtaining useful knowledge to reason over. To address these issues, we propose a new KG-augmented model: Hybrid Graph Network (HGN). Unlike prior methods, HGN learns to jointly contextualize extracted and generated knowledge by reasoning over both within a unified graph structure. Given the task input context and an extracted KG subgraph, HGN is trained to generate embeddings for the subgraph's missing edges to form a "hybrid" graph, then reason over the hybrid graph while filtering out context-irrelevant edges. We demonstrate HGN's effectiveness through considerable performance gains across four commonsense reasoning benchmarks, plus a user study on edge validness and helpfulness.
Code (1)
Tasks
Knowledge GraphsNatural Language InferenceQuestion AnsweringSimilar Papers 제목 키워드 기반
Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning
Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art resul…
Knowledge GraphsNatural Language InferenceQuestion AnsweringVLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge
There has been a growing interest in solving Visual Question Answering (VQA) tasks that require the model to reason beyond the content present in the image. In this work, we focus on questions that require commonsense re…
Question AnsweringVisual Question AnsweringVisual Question Answering (VQA)CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning
Commonsense reasoning, aiming at endowing machines with a human-like ability to make situational presumptions, is extremely challenging to generalize. For someone who barely knows about "meditation," while is knowledgeab…
CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues
This paper addresses the problem of dialogue reasoning with contextualized commonsense inference. We curate CICERO, a dataset of dyadic conversations with five types of utterance-level reasoning-based inferences: cause, …
Answer GenerationAnswer SelectionCOLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective
Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, explo…
Causal InferenceCoLACommonsense Causal Reasoning