Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
| Autor: | Edouard Grave, Tomas Mikolov, Piotr Bojanowski, Hervé Jégou, Armand Joulin |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Computation and Language business.industry Computer science Inference Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) 02 engineering and technology computer.software_genre Translation (geometry) Machine Learning (cs.LG) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence State (computer science) business computer Computation and Language (cs.CL) Natural language processing Word (computer architecture) |
| Zdroj: | EMNLP |
| Popis: | Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space. Existing works typically solve a least-square regression problem to learn a rotation aligning a small bilingual lexicon, and use a retrieval criterion for inference. In this paper, we propose an unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion. Our experiments on standard benchmarks show that our approach outperforms the state of the art on word translation, with the biggest improvements observed for distant language pairs such as English-Chinese. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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