Articles

In this paper we explore the utility of Machine Translation as a writing aid and its impact on the quality of the text produced. We focus on medical practitioners who are native speakers of Spanish and who need to publish their scientific work in English as a foreign language. After …...

The present paper describes and presents ongoing research on machine translation (MT) and linguistic intercomprehension. One main goal, although not the only one, is to evaluate three machine translation (MT) systems —Systran, Google Translate and Apertium— through an analysis of the readers’ ability to understand the output generated. We compare …...

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, NMT cannot handle a larger vocabulary because the training complexity and decoding complexity proportionally increase with the number of target …...

Although classifiers/quantifiers (CQs) expressions appear frequently in everyday communi- cations or written documents, they are described neither in classical bilingual paper dictio- naries, nor in machine-readable dictionaries. The paper describes a CQs dictionary, edited from the corpus we have annotated, and its usage in the framework of French-Japanese ma- chine …...

Elastic-substitution decoding (ESD), first introduced by Chiang (2010), can be important for obtaining good results when applying labels to enrich hierarchical statistical machine translation (SMT). However, an efficient implementation is essential for scalable application. We describe how to achieve this, contributing essential details that were missing in the original exposition. …...

We present the second ever evaluated Arabic dialect-to-dialect machine translation effort, and the first to leverage external resources beyond a small parallel corpus. The subject has not previously received serious attention due to lack of naturally occurring parallel data; yet its importance is evidenced by dialectal Arabic’s wide usage and …...

We describe a data selection method for domain adaptation in machine translation, based on relative frequency ratios computed between in-domain and out-of-domain corpora. Our method is compared to a state-of-the-art approach based on cross-entropy differences, outperforming it significantly in terms of data sparseness reduction and BLEU scores on the models …...

Neural machine translation is a recently proposed approach which has shown competitive re- sults to traditional MT approaches. Similar to other neural network based methods, NMT also suffers from low performance for the domains with less available training data. Domain adaptation deals with improving performance of a model trained on …...

This study investigates the coordination of reading (input) and writing (output) activities in from-scratch translation and post-editing. We segment logged eye movements and keylogging data into minimal units of reading and writing activity and model the process of post-editing and from-scratch translation as a Markov model. We show that the …...

Methods to predict the effort needed to post-edit a given machine translation (MT) output are seen as a promising direction to making MT more useful in the translation industry. Despite the wide variety of approaches that have been proposed, with increasing complexity as regards their number of features and parameters, …...

Interactive machine translation research has focused primarily on predictive typing, which re- quires a human to type parts of the translation. This paper explores an interactive setting in which humans guide the attention of a neural machine translation system in a manner that requires no text entry at all. The …...

Interactive machine translation research has focused primarily on predictive typing, which re- quires a human to type parts of the translation. This paper explores an interactive setting in which humans guide the attention of a neural machine translation system in a manner that requires no text entry at all. The …...

In this paper, we explore a simple solution to “Multi-Source Neural Machine Translation" (MSNMT) which only relies on preprocessing a N-way multilingual corpus without modifying the Neural Machine Translation (NMT) architecture or training procedure. We simply concate- nate the source sentences to form a single long multi-source input sentence while …...

Processing of multi-word expressions (MWEs) is a known problem for any natural language processing task. Even neural machine translation (NMT) struggles to overcome it. This paper presents results of experiments on investigating NMT attention allocation to the MWEs and improving automated translation of sentences that contain MWEs in English→Latvian and …...

Recent years have seen unprecedented growth in the use of MT across industries and domains. Partly this is due to the ready availability of open source MT tools such as Moses or online or customizable services. It is also due to fundamental shifts in the technology, specifically the move to …...

This paper introduces a unique large-scale machine translation dataset with various levels of human annotation combined with automatically recorded productivity features such as time and keystroke logging and manual scoring during the annotation process. The data was collected as part of the EU-funded QT21 project and comprises 20,000–45,000 sentences of …...

In this paper, we discuss different methods which use meta information and richer context that may accompany source language input to improve machine translation quality. We focus on category information of input text as meta information, but the proposed methods can be extended to all textual and non-textual meta information …...

Hybrid machine translation (HMT) takes advantage of different types of machine translation (MT) systems to improve translation performance. Neural machine translation (NMT) can produce more fluent translations while phrase-based statistical machine translation (PB-SMT) can produce adequate results primarily due to the contribution of the translation model. In this paper, we …...

AbstractThis paper presents a new classification of verbs of change and modification, proposing a dynamic interpretation of the lexical semantics of the predicate and its arguments. Adopting the model of dynamic event structure proposed in Pustejovsky (2013), and extending the model of dynamic selection outlined in Pustejovsky and Jezek (2011), …...

AbstractWe consider the extent to which different deep neural network (DNN) configurations can learn syntactic relations, by taking up Linzen et al.’s (2016) work on subject-verb agreement with LSTM RNNs. We test their methods on a much larger corpus than they used (a ⇠24 million example part of the WaCky …...