Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning “difficult” concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20% of training time.

Cet article présente un système d’alertes fondé sur la masse de données issues de Tweeter. L’objectif de l’outil est de surveiller l’actualité, autour de différents domaines témoin incluant les événements sportifs ou les catastrophes naturelles. Cette surveillance est transmise à l’utilisateur sous forme d’une interface web contenant la liste d’événements localisés sur une carte.

Since most Korean postpositions signal grammatical functions such as syntactic relations, generation of incorrect Korean post-positions results in producing ungrammatical outputs in machine translations targeting Korean. Chinese and Korean belong to morphosyntactically divergent language pairs, and usually Korean postpositions do not have their counterparts in Chinese. In this paper, we propose a preprocessing method for a statistical MT system that generates more adequate Korean postpositions. We transfer syntactic relations of subject-verb-object patterns in Chinese sentences and enrich them with transferred syntactic relations in order to reduce the morpho-syntactic differences. The effectiveness of our proposed method is measured with lexical units of various granularities. Human evaluation also suggest improvements over previous methods, which are consistent with the result of the automatic evaluation.

We propose a Chinese dependency tree reordering method for Chinese-to-Korean SMT systems through analyzing systematic differences between the Chinese and Korean languages. Translating predicate-predicate patterns in Chinese into Korean raises various issues such as long-distance reordering. This paper concentrates on syntactic reordering of predicate-predicate patterns in Chinese dependency trees through contrastively analyzing construction types in Chinese and their corresponding translations in Korean. We explore useful linguistic knowledge that assists effective syntactic reordering of Chinese dependency trees; we design two experiments with different kinds of linguistic knowledge combined with the phrase and hierarchical phrase-based SMT systems, and assess the effectiveness of our proposed methods. The experiments achieved significant improvements by resolving the long-distance reordering problem.