Neural Network Language Models for Translation with Limited Data
Maxim Khalilov, José A. R. Fonollosa, Francisco Zamora-Martínez, María José Castro-Bleda, Salvador España-Boquera · 2008
In this paper we present how to estimate a continuous space Language Model with a Neural Network to be used in a Statistical Machine Translation system. We report results for an Italian-English translation task obtained on a small corpus (about 150~K tokens), that can be considered a task with a lack of training data. Different word history length included in the connectionist language model (Ngram order) and distinct continuous space representation (i.e. words appearing in the training corpus more than k times) are considered in the study. The experimental results are evaluated by means of automatic evaluation metrics correlated with fluency and adequacy of the generated translations.