This paper describes the Post-Editor Z system submitted to the L2 writing assistant task in SemEval-2014. The aim of task is to build a translation assistance system to translate untranslated sentence fragments. This is not unlike the task of post-editing where human translators improve machine-generated translations. Post-Editor Z emulates the manual process of post-editing by (i) crawling and...

In this paper, we present hybrid decoding — a novel statistical machine translation (SMT) decoding paradigm using multiple SMT systems. In our work, in addition to component SMT systems, system combination method is also employed in generating partial translation hypotheses throughout the decoding process, in which smaller hypotheses generated by each component decoder and hypotheses combinatio...

This paper presents an approach to translating Chinese organization names into English based on correlative expansion. Firstly, some candidate translations are generated by using statistical translation method. And several correlative named entities for the input are retrieved from a correlative named entity list. Secondly, three kinds of expansion methods are used to generate some expanded que...

Current machine translation systems require human revision to produce high-quality translations. This is achieved through a post-editing process or by means of an interactive human–computer collaboration. Most protocols belonging to the last scenario follow a left-to-right strategy, where the prefix of the translation is iteratively increased by successive validations and corrections made by th...

In this paper we focus on the incremental decoding for a statistical phrase-based machine translation system. In incremental decoding, translations are generated incrementally for every word typed by a user, instead of waiting for the entire sentence as input. We introduce a novel modification to the beam-search decoding algorithm for phrase-based MT to address this issue, aimed at efficient co...

This paper reports experiments on adapting components of a Statistical Machine Translation (SMT) system for the task of translating online user-generated forum data from Symantec. Such data is monolingual, and differs from available bitext MT training resources in a number of important respects. For this reason, adaptation techniques are important to achieve optimal results. We investigate the ...

This paper describes a method that successfully exploits simple syntactic features for n-best translation candidate reranking using perceptrons. Our approach uses discriminative language modelling to rerank the nbest translations generated by a statistical machine translation system. The performance is evaluated for Arabic-to-English translation using NIST’s MT-Eval benchmarks. Whilst parse tre...

We are experimenting to examine how AnglaBharati system designed to translate English to Indian languages could be adapted for translation to Sanskrit. The main contribution of our work is demonstration of machine translation of English to Sanskrit for simple sentences based on PLIL generated by AnglaBharati and Ashtadhyayi rules. Presently our translation system caters to affirmative, negative...

Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information. In this work, we propose to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history. The probability distribution over generated words is updated onli...

In statistical machine translation, many of the top-performing systems are phrase-based systems. This paper describes a phrase-based translation system and some improvements. We use more information to compute translation probability. The scaling factors of the log-linear models are estimated by the minimum error rate training that uses an evaluation criteria to balance BLEU and NIST scores. We...