Translation Task

This paper presents the results of the WMT09 shared tasks, which included a translation task, a system combination task, and an evaluation task. We con- ducted a large-scale manual evaluation …...

We describe refinements to hierarchical translation search procedures intended to reduce both search errors and memory us- age through modifications to hypothesis expansion in cube pruning and reductions in the …...

This paper presents the results of the WMT10 and MetricsMATR10 shared tasks,1 which included a translation task, a system combination task, and an eval- uation task. We conducted a large-scale …...

Statistical machine translation relies heav- ily on parallel corpora to train its mod- els for translation tasks. While more and more bilingual corpora are readily avail- able, the quality of …...

We describe our system for the translation task of WMT 2010. This system, devel- oped for the English-French and French- English directions, is based on Moses and was trained using …...

This paper presents the results of the WMT11 shared tasks, which included a translation task, a system combination task, and a task for machine translation evaluation metrics. We conducted a …...

We present the S-SSTC framework for machine translation (MT), introduced in 2002 and developed since as a set of working MT systems (SiSTeC-ebmt). Our approach is example-based, but differs from …...

This paper describes the UPM system for translation task at the EMNLP 2011 workshop on statistical machine translation (http://www.statmt.org/wmt11/), and it has been used for both directions: Spanish-English and English-Spanish. …...

This paper presents the results of the WMT12 shared tasks, which included a translation task, a task for machine translation evaluation metrics, and a task for run-time estimation of machine …...

Social media texts are written in an infor- mal style, which hinders other natural lan- guage processing (NLP) applications such as machine translation. Text normalization is thus important for processing …...

We present a simple log-linear reparame- terization of IBM Model 2 that overcomes problems arising from Model 1’s strong assumptions and Model 2’s overparame- terization. Efficient inference, likelihood evaluation, and …...

We present the results of the WMT13 shared tasks, which included a translation task, a task for run-time estimation of ma- chine translation quality, and an unoffi- cial metrics task. …...

Training discriminative rule selection models is usually expensive because of the very large size of the hierarchical gram- mar. Previous approaches reduced the training costs either by (i) using mod- …...

AbstractIn order to make the growing amount of conceptual knowledge available through ontologies and datasets accessible to humans, NLP applications need access to information on how this knowledge can be …...

AbstractLong-distance semantic dependencies are crucial for lexical choice in statistical machine translation. In this paper, we study semantic dependencies between verbs and their arguments by modeling selectional preferences in the …...

AbstractThe overarching objective underlying this research is to develop an online tool, based on a parallel corpus of French Belgian Sign Language (LSFB) and written Belgian French. This tool is …...

AbstractRecently, the development of neural machine translation (NMT) has significantly improved the translation quality of automatic machine translation. While most sentences are more accurate and fluent than translations by statistical …...

Neural machine translation (NMT), a new approach to machine translation, has achieved promising results comparable to those of traditional approaches such as statistical machine translation (SMT). Despite its recent success, …...

Superiority of neural machine translation (NMT) and phrase-based statistical machine transla- tion (PBSMT) depends on the translation task. For some translation tasks, such as those involv- ing low-resource language pairs …...

AbstractNatural language sentences, being hierarchical, can be represented at different levels of granularity, like words, subwords, or characters. But most neural machine translation systems require the sentence to be represented …...

AbstractMost modern neural machine translation (NMT) systems rely on presegmented inputs. Segmentation granularity importantly determines the input and output sequence lengths, hence the modeling depth, and source and target vocabularies, …...

AbstractThis study proposes a new neural machine translation (NMT) model based on the encoder-decoder model that incorporates named entity (NE) tags of source-language sentences. Conventional NMT models have two problems …...

AbstractWe explore multitask models for neural translation of speech, augmenting them in order to reflect two intuitive notions. First, we introduce a model where the second task decoder receives information …...

Adapting machine translation systems in the real world is a difficult problem. In contrast to offline training, users cannot provide the type of fine-grained feedback (such as correct translations) typically …...

AbstractIn this paper, we tested 20 Brazilian Portuguese speakers at intermediate and advanced English proficiency levels to investigate the influence of Google Translate’s MT system on the mental processing of …...

AbstractThe Neural Machine Translation (NMT) model is essentially a joint language model conditioned on both the source sentence and partial translation. Therefore, the NMT model naturally involves the mechanism of …...

AbstractUnderstanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts. However, it is generally difficult to fully …...

AbstractK-Nearest Neighbor Neural Machine Translation (kNNMT) successfully incorporates external corpus by retrieving word-level representations at test time. Generally, kNNMT borrows the off-the-shelf context representation in the translation task, e.g., the …...