How much hybridisation does machine translation need?

This is the peer reviewed version of the following article: [Costa-jussà, M. R. (2015), How much hybridization does machine translation Need?. J Assn Inf Sci Tec, 66: 2160–2165. doi:10.1002/asi.23517], which has been published in final form at [10.1002/asi.23517]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Rule-based and corpus-based machine translation (MT)have coexisted for more than 20 years. Recently, bound-aries between the two paradigms have narrowed andhybrid approaches are gaining interest from bothacademia and businesses. However, since hybridapproaches involve the multidisciplinary interaction oflinguists, computer scientists, engineers, and informa-tion specialists, understandably a number of issuesexist.While statistical methods currently dominate researchwork in MT, most commercial MT systems are techni-cally hybrid systems. The research community shouldinvestigate the bene¿ts and questions surrounding thehybridization of MT systems more actively. This paperdiscusses various issues related to hybrid MT includingits origins, architectures, achievements, and frustra-tions experienced in the community. It can be said thatboth rule-based and corpus- based MT systems havebene¿ted from hybridization when effectively integrated.In fact, many of the current rule/corpus-based MTapproaches are already hybridized since they do includestatistics/rules at some point.Peer ReviewedPostprint (author's final draft

Statistical Machine Translation Features with Multitask Tensor Networks

We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose new features based on neural networks to model various non-local translation phenomena. Second, we augment the architecture of the neural network with tensor layers that capture important higher-order interaction among the network units. Third, we apply multitask learning to estimate the neural network parameters jointly. Each of our proposed methods results in significant improvements that are complementary. The overall improvement is +2.7 and +1.8 BLEU points for Arabic-English and Chinese-English translation over a state-of-the-art system that already includes neural network features.Comment: 11 pages (9 content + 2 references), 2 figures, accepted to ACL 2015 as a long pape

A tree does not make a well-formed sentence: Improving syntactic string-to-tree statistical machine translation with more linguistic knowledge

AbstractSynchronous context-free grammars (SCFGs) can be learned from parallel texts that are annotated with target-side syntax, and can produce translations by building target-side syntactic trees from source strings. Ideally, producing syntactic trees would entail that the translation is grammatically well-formed, but in reality, this is often not the case. Focusing on translation into German, we discuss various ways in which string-to-tree translation models over- or undergeneralise. We show how these problems can be addressed by choosing a suitable parser and modifying its output, by introducing linguistic constraints that enforce morphological agreement and constrain subcategorisation, and by modelling the productive generation of German compounds

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

The integration of machine translation and translation memory

We design and evaluate several models for integrating Machine Translation (MT) output into a Translation Memory (TM) environment to facilitate the adoption of MT technology in the localization industry. We begin with the integration on the segment level via translation recommendation and translation reranking. Given an input to be translated, our translation recommendation model compares the output from the MT and the TMsystems, and presents the better one to the post-editor. Our translation reranking model combines k-best lists from both systems, and generates a new list according to estimated post-editing effort. We perform both automatic and human evaluation on these models. When measured against the consensus of human judgement, the recommendation model obtains 0.91 precision at 0.93 recall, and the reranking model obtains 0.86 precision at 0.59 recall. The high precision of these models indicates that they can be integrated into TM environments without the risk of deteriorating the quality of the post-editing candidate, and can thereby preserve TM assets and established cost estimation methods associated with TMs. We then explore methods for a deeper integration of translation memory and machine translation on the sub-segment level. We predict whether phrase pairs derived from fuzzy matches could be used to constrain the translation of an input segment. Using a series of novel linguistically-motivated features, our constraints lead both to more consistent translation output, and to improved translation quality, reflected by a 1.2 improvement in BLEU score and a 0.72 reduction in TER score, both of statistical significance (p < 0.01). In sum, we present our work in three aspects: 1) translation recommendation and translation reranking models that can access high quality MT outputs in the TMenvironment, 2) a sub-segment translation memory and machine translation integration model that improves both translation consistency and translation quality, and 3) a human evaluation pipeline to validate the effectiveness of our models with human judgements