Current approaches to cross-lingual information retrieval (CLIR) rely on standard retrieval
models into which query translations by statistical machine translation (SMT) are integrated …

Parameter tuning is an important problem in statistical machine translation, but surprisingly,
most existing methods such as MERT, MIRA and PRO are agnostic about search, while …

The environmental and social costs of using oil resources as well as their increasing scarcity
are challenging tomorrow's agriculture to reduce its dependency on fossil energies (Chow et …

Current syntactic machine translation (MT) systems implicitly use beam-width unlimited
search in learning model parameters (eg, feature values for each translation rule). However …

Automatic translation of natural language is still (as of 2017) a long-standing but unmet
promise. While advancing at a fast rate, the underlying methods are still far from actually …

Durant ces dernières années, les architectures de réseaux de neurones (RN) ont été
appliquées avec succès à de nombreuses applications en Traitement Automatique de …

Discriminative training as a general machine learning approach has wide applications in
tasks like Natural Language Processing (NLP) and Automatic Speech Recognition (ASR). In …

Parameter tuning is a key problem for statistical machine translation (SMT). Most popular
parameter tuning algorithms for SMT are agnostic of decoding, resulting in parameters …

Typical MT training involves several stages, including word alignment, rule extraction,
translation model estimation, and parameter tuning. In this paper, different from the …