| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560419 |
| ISSN: | 1405-5546 |
| Autors: | Pham, Thuong Hai1 Macháček, Dominik1 Bojar, Ondřej1 |
| Institucions: | 1Charles University, Faculty of Mathematics and Physics, Prague. República Checa |
| Any: | 2019 |
| Període: | Jul-Sep |
| Volum: | 23 |
| Número: | 3 |
| Paginació: | 923-934 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | The utility of linguistic annotation in neural machine translation seemed to had been established in past papers. The experiments were however limited to recurrent sequence-to-sequence architectures and relatively small data settings. We focus on the state-of-the-art Transformer model and use comparably larger corpora. Specifically, we try to promote the knowledge of source-side syntax using multi-task learning either through simple data manipulation techniques or through a dedicated model component. In particular, we train one of Transformer attention heads to produce source-side dependency tree. Overall, our results cast some doubt on the utility of multi-task setups with linguistic information. The data manipulation techniques, recommended in previous works, prove ineffective in large data settings. The treatment of self-attention as dependencies seems much more promising: it helps in translation and reveals that Transformer model can very easily grasp the syntactic structure. An important but curious result is, however, that identical gains are obtained by using trivial "linear trees" instead of true dependencies. The reason for the gain thus may not be coming from the added linguistic knowledge but from some simpler regularizing effect we induced on self-attention matrices. |
| Disciplines | Ciencias de la computación |
| Paraules clau: | Inteligencia artificial |
| Keyword: | Syntax, Transformer NMT, Multi-Task NMT, Artificial intelligence |
| Text complet: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
