2018/10/21 by Ta-Chung Chi, Chi, Ta-Chung, Ching-Yen Shih +3 · 2 citations
Computer Science · #Advanced Text Analysis Techniques #Artificial intelligence #Benchmark (surveying) #Computation and Language (cs.CL) #Computer science #Consistency (knowledge bases) #Embedding #FOS: Computer and information sciences #Geography #Image (mathematics) #Linguistics #Natural Language Processing Techniques #Natural language processing #Similarity (geometry) #Task (project management) #Topic Modeling #Word (group theory) #Word embedding #cs.CL
paper · pdf · doi:10.48550/arxiv.1810.08951
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/10/21 · openalex publication_date 2018/10/21 · arxiv updated 2018/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
This paper introduces the first dataset for evaluating English-Chinese Bilingual Contextual Word Similarity, namely BCWS (https://github.com/MiuLab/BCWS). The dataset consists of 2,091 English-Chinese word pairs with the corresponding sentential contexts and their similarity scores annotated by the human. Our annotated dataset has higher consistency compared to other similar datasets. We establish several baselines for the bilingual embedding task to benchmark the experiments. Modeling cross-lingual sense representations as provided in this dataset has the potential of moving artificial intelligence from monolingual understanding towards multilingual understanding.