Learn More
In this paper, we propose a novel neu-ral network model called RNN Encoder– Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and de-coder of the proposed model are jointly(More)
This paper describes the three systems developed by the LIUM for the IWSLT 2011 evaluation campaign. We participated in three of the proposed tasks, namely the Automatic Speech Recognition task (ASR), the ASR system combination task (ASR_SC) and the Spoken Language Translation task (SLT), since these tasks are all related to speech translation. We present(More)
1 RNN Encoder–Decoder In this document, we describe in detail the architecture of the RNN Encoder–Decoder used in the experiments. Each phrase is a sequence of K-dimensional one-hot vectors, such that only one element of the vector is 1 and all the others are 0. The index of the active (1) element indicates the word represented by the vector.
This paper presents the systems developed by LIUM and CVC for the WMT16 Mul-timodal Machine Translation challenge. We explored various comparative methods , namely phrase-based systems and at-tentional recurrent neural networks models trained using monomodal or multi-modal data. We also performed a human evaluation in order to estimate the usefulness of(More)
Recent works on end-to-end neural network-based architectures for machine translation have shown promising results for English-French and English-German translation. Unlike these language pairs, however, in the majority of scenarios, there is a lack of high quality parallel corpora. In this work, we focus on applying neural machine translation to(More)
We present a new approach for neural machine translation (NMT) using the morphological and grammatical decomposition of the words (factors) in the output side of the neural network. This architecture addresses two main problems occurring in MT, namely dealing with a large target language vocabulary and the out of vocabulary (OOV) words. By the means of(More)
We present novel features designed with a deep neural network for Machine Translation (MT) Quality Estimation (QE). The features are learned with a Continuous Space Language Model to estimate the probabilities of the source and target segments. These new features, along with standard MT system-independent features, are benchmarked on a series of datasets(More)