Name
Abstract | ||
|---|---|---|
This paper shows the utility of two open-source tools designed for parallel data cleaning: Bifixer and Bicleaner. Already used to clean highly noisy parallel content from crawled multilingual websites, we evaluate their performance in a different scenario: cleaning publicly available corpora commonly used to train machine translation systems. We choose four English–Portuguese corpora which we plan to use internally to compute paraphrases at a later stage. We clean the four corpora using both tools, which are described in detail, and analyse the effect of some of the cleaning steps on them. We then compare machine translation training times and quality before and after cleaning these corpora, showing a positive impact particularly for the noisiest ones. |
Year | Venue | DocType |
|---|---|---|
2020 | EAMT | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gema Ramírez-Sánchez | 1 | 0 | 3.38 |
| Jaume Zaragoza-Bernabeu | 2 | 0 | 0.68 |
| Marta Bañón | 3 | 0 | 1.01 |
| Sergio Ortiz-Rojas | 4 | 149 | 17.22 |