arXiv:2006.01414
공식 코드 · Papers with Code
Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data
원문 초록
This paper presents the system used in our submission to the \textit{IWPT 2020 Shared Task}. Our system is a graph-based parser with second-order inference. For the low-resource Tamil corpus, we specially mixed the training data of Tamil with other languages and significantly improved the performance of Tamil. Due to our misunderstanding of the submission requirements, we submitted graphs that are not connected, which makes our system only rank \textbf{6th} over 10 teams. However, after we fixed this problem, our system is 0.6 ELAS higher than the team that ranked \textbf{1st} in the official results.
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An Aspect Extraction Framework using Different Embedding Types, Learning Models, and Dependency Structure
Multi-View Attention Syntactic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis
Top General Performance = Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark
Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency Parsing
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