SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation
Google (United States) · George Washington University · +2 more institutions
Abstract
Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing…
Citation impact
- FWCI
- 18.52
- Percentile
- 100%
- References
- 7
Authors
5- DCDaniel CerCorresponding
Google (United States)
- MDMona Diab
George Washington University
- EAEneko Agirre
University of the Basque Country
- ILInigo Lopez-Gazpio
University of the Basque Country
- LSLucia Specia
University of Sheffield
Topics & keywords
- Task (project management)
- Semantic similarity
- Similarity (geometry)
- Machine translation
- Dialog box
- Meaning (existential)
- Set (abstract data type)
- Selection (genetic algorithm)