KoBE: Knowledge-Based Machine Translation Evaluation
| Autor: | Roee Aharoni, Genady Beryozkin, Markus Freitag, Wolfgang Macherey, Zorik Gekhman |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Machine translation business.industry Computer science 02 engineering and technology computer.software_genre Translation (geometry) Task (project management) Knowledge base 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business Computation and Language (cs.CL) computer Sentence Natural language processing BLEU |
| Zdroj: | EMNLP (Findings) |
| Popis: | We propose a simple and effective method for machine translation evaluation which does not require reference translations. Our approach is based on (1) grounding the entity mentions found in each source sentence and candidate translation against a large-scale multilingual knowledge base, and (2) measuring the recall of the grounded entities found in the candidate vs. those found in the source. Our approach achieves the highest correlation with human judgements on 9 out of the 18 language pairs from the WMT19 benchmark for evaluation without references, which is the largest number of wins for a single evaluation method on this task. On 4 language pairs, we also achieve higher correlation with human judgements than BLEU. To foster further research, we release a dataset containing 1.8 million grounded entity mentions across 18 language pairs from the WMT19 metrics track data. Comment: Accepted as a short paper in Findings of EMNLP 2020 |
| Databáze: | OpenAIRE |
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