Loading...
Please wait, while we are loading the content...
Similar Documents
On the Use of Morpho-Syntactic Description Tags in Neural Machine Translation with Small and Large Training Corpora
| Content Provider | MDPI |
|---|---|
| Author | Mirjam, Sepesy Maučec Donaj, Gregor |
| Copyright Year | 2022 |
| Description | With the transition to neural architectures, machine translation achieves very good quality for several resource-rich languages. However, the results are still much worse for languages with complex morphology, especially if they are low-resource languages. This paper reports the results of a systematic analysis of adding morphological information into neural machine translation system training. Translation systems presented and compared in this research exploit morphological information from corpora in different formats. Some formats join semantic and grammatical information and others separate these two types of information. Semantic information is modeled using lemmas and grammatical information using Morpho-Syntactic Description (MSD) tags. Experiments were performed on corpora of different sizes for the English–Slovene language pair. The conclusions were drawn for a domain-specific translation system and for a translation system for the general domain. With MSD tags, we improved the performance by up to 1.40 and 1.68 BLEU points in the two translation directions. We found that systems with training corpora in different formats improve the performance differently depending on the translation direction and corpora size. |
| Starting Page | 1608 |
| e-ISSN | 22277390 |
| DOI | 10.3390/math10091608 |
| Journal | Mathematics |
| Issue Number | 9 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-09 |
| Access Restriction | Open |
| Subject Keyword | Mathematics Neural Machine Translation Pos Tags Msd Tags Inflected Language Data Sparsity Corpora Size |
| Content Type | Text |
| Resource Type | Article |
