End-to-End Neural Word Alignment Outperforms GIZA++
| Autor: | Thomas Zenkel, Joern Wuebker, John DeNero |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Machine translation Computer science Speech recognition 05 social sciences 010501 environmental sciences computer.software_genre Lexicon 01 natural sciences End-to-end principle 0502 economics and business Unsupervised learning 050207 economics Computation and Language (cs.CL) computer 0105 earth and related environmental sciences Transformer (machine learning model) |
| Zdroj: | ACL |
| Popis: | Word alignment was once a core unsupervised learning task in natural language processing because of its essential role in training statistical machine translation (MT) models. Although unnecessary for training neural MT models, word alignment still plays an important role in interactive applications of neural machine translation, such as annotation transfer and lexicon injection. While statistical MT methods have been replaced by neural approaches with superior performance, the twenty-year-old GIZA++ toolkit remains a key component of state-of-the-art word alignment systems. Prior work on neural word alignment has only been able to outperform GIZA++ by using its output during training. We present the first end-to-end neural word alignment method that consistently outperforms GIZA++ on three data sets. Our approach repurposes a Transformer model trained for supervised translation to also serve as an unsupervised word alignment model in a manner that is tightly integrated and does not affect translation quality. Accepted at ACL 2020 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|