Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension
This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment the input with relation embedding from the graph of general knowledge ConceptNet (Speer et al., 2017). As a result, our system achieves state-of-the-art performance with 83.95% accuracy on the official test data. Code is publicly available at https://github.com/intfloat/commonsense-rc
Code (2)
Tasks
General KnowledgeReading ComprehensionSimilar Papers 제목 키워드 기반
SemEval-2012 Task 2: Measuring Degrees of Relational Similarity
Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge
Luminoso participated in the SemEval 2018 task on "Capturing Discriminative Attributes" with a system based on ConceptNet, an open knowledge graph focused on general knowledge. In this paper, we describe how we trained a…
General KnowledgeRelation ExtractionConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge
This paper describes Luminoso's participation in SemEval 2017 Task 2, "Multilingual and Cross-lingual Semantic Word Similarity", with a system based on ConceptNet. ConceptNet is an open, multilingual knowledge graph that…
General KnowledgeMultilingual Word EmbeddingsTask 2Word Embeddings+1Relational Graph Attention Network for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opi…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Graph AttentionSentence+1ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets
This paper presents a UIR-Miner system for emotion and sentiment analysis evaluation in Twitter in SemEval 2018. Our system consists of three main modules: preprocessing module, stacking module to solve the intensity pre…
ClassificationEmotion ClassificationGeneral ClassificationMulti-Label Classification+3