Exploring lifelong learning in neural machine translation
Lifelong learning is a technique defined as the ability to learn newor maintain older knowledge over time. This factor is very important for humans, as it is one of the keys to the ability to learn by humans, which artificial intelligence tries to replicate. Additionally, it supposes the possibility...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/360304 |
| Acceso en línea: | https://hdl.handle.net/2117/360304 |
| Access Level: | acceso abierto |
| Palabra clave: | Active learning Machine translating Deep learning lifelong learning deep learning quality estimation machine translation active learning Aprenentatge actiu Traducció automàtica Aprenentatge profund Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| Sumario: | Lifelong learning is a technique defined as the ability to learn newor maintain older knowledge over time. This factor is very important for humans, as it is one of the keys to the ability to learn by humans, which artificial intelligence tries to replicate. Additionally, it supposes the possibility for the systems to adapt to new inputs without the necessity to train the models from scratch every time. This technique, although being already applied on autonomous agents or machine learning for computer vision, has not been evaluated in the field of Machine Translation (MT). In the case of MT, currently, the majority of processes are still using sets of static data or traditional techniques which force to train the model only once without taking into account the possible variation of the language in the context. This project will be carried out on the context of a system implemented on the BEAT platform and thought for the evaluation of a lifelong learning task, which uses two sets of data: one for training and the other to apply the learning without the translations, having a simulated person to whom we can request the translations for the sentences we think are necessary. The objective is to suggest and analyze the usage of an active learning technique: Quality Estimation (QE), and the following comparison with the results obtained using random selection. In this project, we work with the language pairs of English-French and English-German. As results with QEwe achieve a score of 26.7 and 15.9 points for EN-FR and EN-DE, respectively, using a penalizing n-grams precisions BLEU score. These means an improvement of 0,5 for ENFR and 0,7 for EN-DE over the results obtained using random selection. In conclusion, the usage of lifelong learning in machine translation is feasible, although still is in an initial phase. As future possible actions over QE would be interesting to make a more extensive search of parameters or using an adaptive QE model. |
|---|