Articles

AbstractThe multi-level adaptive networks (MLAN) technique is a cross-lingual adaptation framework where a bottleneck (BN) layer in a deep neural network (DNN) trained in a source language is used for producing BN features to be exploited in a second DNN in a target language. We investigate how the correlation (in …...

AbstractIn an increasingly globalized world, there is a rising demand for speech recognition systems. Systems for languages like English, German or French do achieve a decent performance, but there exists a long tail of languages for which such systems do not yet exist. State-of-the-art speech recognition systems feature Deep Neural …...

AbstractThis paper describes a two-step machine translation system that addresses the issue of translating into a morphologically rich language (English to Czech), by performing separately the translation and the generation of target morphology. The first step consists in translating from English into a normalized version of Czech, where some morphological …...

AbstractIn this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach in which the information shared among languages can be helpful in the translation of individual language pairs. We are then able to employ attention-based Neural Machine Translation for many-to-many multilingual …...

AbstractString-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target-side context. The lack of context is one reason why verb translation recall is as low as 45.5%. We propose a verb lexicon model trained with a feed-forward neural network that predicts the …...

AbstractIn this paper we provide the largest published comparison of translation quality for phrase-based SMT and neural machine translation across 30 translation directions. For ten directions we also include hierarchical phrase-based MT. Experiments are performed for the recently published United Nations Parallel Corpus v1.0 and its large six-way sentence-aligned subcorpus. …...

AbstractSpeech that contains multimedia content can pose a serious challenge for real-time automatic speech recognition (ASR) for two reasons: (1) The ASR produces meaningless output, hurting the readability of the transcript. (2) The search space of the ASR is blown up when multimedia content is encountered, resulting in large delays …...

AbstractIn this paper we investigate the potential of the neural machine translation (NMT) when taking into consideration the linguistic aspect of target language. From this standpoint, the NMT approach with attention mechanism [1] is extended in order to produce several linguistically derived outputs. We train our model to simultaneously output …...

AbstractNeural models have recently shown big improvements in the performance of phrase-based machine translation. Recurrent language models, in particular, have been a great success due to their ability to model arbitrary long context. In this work, we integrate global semantic information extracted from large encyclopedic sources into neural network language …...

AbstractThe IWSLT 2016 Evaluation Campaign featured two tasks: the translation of talks and the translation of video conference conversations. While the first task extends previously offered tasks with talks from a different source, the second task is completely new. For both tasks, three tracks were organised: automatic speech recognition (ASR), …...

This paper describes the VCU systems that participated in the Semantic Taxonomy En- richment task of SemEval 2016. The three systems are unsupervised and relied on dictionary-based similarity measures. The first two runs used first-order measures (Lesk and First-order vector), and the third run used a second-order measure (Second-order vec- …...

This paper presents a solution to Semeval 2016 Task 14 which asks for a system that is able to insert new lemmas into WordNet. Our system aims to do this by overlapping words in the definitions of the to-be-inserted lemma and all senses in WordNet. This paper includes the results …...

In this paper we describe the participation of the Joint Research Centre, EC, in task 14 - Semantic Taxonomy Enrichment at SemEval 2016. The algorithm which we propose trans- forms each candidate definition into a term vector, where each dimension represents a term and its value is calculated by TF.IDF. …...

Automatic enrichment of semantic tax- onomies with novel data is a relatively unexplored task with potential benefits in a broad array of natural language processing problems. Task 14 of SemEval 2016 poses the challenge of designing systems for this task. In this paper, we describe and evaluate several machine learning …...

AbstractThis paper describes the submission of the University of Edinburgh team to the IWSLT MT task for TED talks. We took part in four translation directions, en-de, de-en, en-fr, and fr-en. The models have been trained with an attentional encoder-decoder model using Nematus, training data filtering and back-translation have been …...

AbstractWe present our submissions to the IWSLT 2016 machine translation task, as our first attempt to translate subtitles and one of our early experiments with neural machine translation (NMT). We focus primarily on English→Czech translation direction but perform also basic adaptation experiments for NMT with German and also the reverse …...

AbstractWe describe the University of Maryland machine translation system submitted to the IWSLT 2016 Microsoft Speech Language Translation (MSLT) English-French task. Our main finding is that translating conversation transcripts turned out to not be as challenging as we expected: while translation quality is of course not perfect, a straightforward phrase-based …...

AbstractThis paper describes our German and English Speech-to-Text (STT) systems for the 2016 IWSLT evaluation campaign. The campaign focuses on the transcription of unsegmented TED talks. Our setup includes systems using both the Janus and Kaldi frameworks. We combined the outputs using both ROVER [1] and confusion network combination (CNC) …...

AbstractThis work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of International Workshop on Spoken Language Translation (IWSLT) 2016. We have participated in the MT track for the German→English language pair employing our state-of-the-art phrase-based system, neural machine translation implementation and our …...

AbstractThis report summarizes the MITLL-AFRL MT and ASR systems and the experiments run during the 2016 IWSLT evaluation campaign. Building on lessons learned from previous years’ results, we refine our ASR systems and examine the explosion of neural machine translation systems and techniques developed in the past year. We experiment …...