Discourse Structure in Machine Translation Evaluation

In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding correlation with human judgments both at the segment- and at the system-level. This suggests that discourse information is complementary to the information used by many of the existing evaluation metrics, and thus it could be taken into account when developing richer evaluation metrics, such as the WMT-14 winning combined metric DiscoTKparty. We also provide a detailed analysis of the relevance of various discourse elements and relations from the RST parse trees for machine translation evaluation. In particular we show that: (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference tree is positively correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse analysis. Computational Linguistics, 201

Selective Attention for Context-aware Neural Machine Translation

Despite the progress made in sentence-level NMT, current systems still fall short at achieving fluent, good quality translation for a full document. Recent works in context-aware NMT consider only a few previous sentences as context and may not scale to entire documents. To this end, we propose a novel and scalable top-down approach to hierarchical attention for context-aware NMT which uses sparse attention to selectively focus on relevant sentences in the document context and then attends to key words in those sentences. We also propose single-level attention approaches based on sentence or word-level information in the context. The document-level context representation, produced from these attention modules, is integrated into the encoder or decoder of the Transformer model depending on whether we use monolingual or bilingual context. Our experiments and evaluation on English-German datasets in different document MT settings show that our selective attention approach not only significantly outperforms context-agnostic baselines but also surpasses context-aware baselines in most cases.Comment: Accepted at NAACL-HLT 201

Skills and Profile of the New Role of the Translator as MT Post-editor

This paper explores the skills and profile of the new role of the translator as MT post-editor in view of the rising interest and use of MT in the translation industry. After a brief review of the relevant literature declaring post-editing (PE) as a profession on its own, the paper goes on to identify the different tasks involved in PE processes, following the work of Krings (Krings, 2001). Then, a series of competences are defined and grouped into three main categories: core competences, linguistic skills and instrumental competences. Finally, a description of the controlled translation scenario of MT PE is advanced taking into account the overall scenario of any translation project, including client description, text domain, text description, use of glossaries, MT engine, MT output quality and purpose of the translated text.Aquest article aborda les habilitats i les característiques del perfil del nou rol del traductor com a posteditor de traducció automàtica, tot i tenint en compte l'augment de l'interès en i l'ús de la traducció automàtica per part de la industria de la traducció. Després d'una breu revisió de la literatura més rellevant sobre postedició (PE) en tant que professió per ella mateixa, l'article identifica les diferents tasques implicades en els processos de PE, segons la proposta de Krings (2001). A continuació es defineix una sèrie de competències que s'agrupen en tres categories principals: competències nuclears, habilitats lingüístiques i competències instrumentals. Finalment el artículo proposa una descripció de l'escenari de traducció controlada propi de la PE de traducció automàtica, sense perdre de vista l'escenari general de qualsevol projecte de traducció, que inclou la descripció del client, el domini del text, la descripció del text, l'ús de glossaris, el motor de traducció automàtica, la qualitat de la traducció automàtica resultant i el propòsit del text traduït.Este artículo aborda las habilidades y las características del perfil del nuevo rol del traductor como poseditor de traducción automática, a la luz del aumento del interés en y del uso de la traducción automática por parte de la industria de la traducción. Después de una breve revisión de la literatura más relevante sobre posedición (PE) en tanto que profesión por sí misma, en el artículo se identifican las diferentes tareas implicadas en los procesos de PE, según la propuesta de Krings (2001). A continuación se define una serie de competencias que se agrupan en tres categorías principales: competencias nucleares, habilidades lingüísticas y competencias instrumentales. Finalmente el artículo propone una descripción del escenario de traducción controlada propio de la PE de traducción automática, sin perder de vista el marco general de cualquier proyecto de traducción, que incluye la descripción del cliente, el dominio del texto, la descripción del texto, el uso de glosarios, el motor de traducción automática, la calidad de la traducción automática resultante y el propósito del texto traducido

On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference

We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world-knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage.Comment: To be presented at NAACL 2018 - 11 page

Are ambiguous conjunctions problematic for machine translation?

The translation of ambiguous words still poses challenges for machine translation. In this work, we carry out a systematic quantitative analysis regarding the ability of different machine translation systems to disambiguate the source language conjunctions “but” and “and”. We evaluate specialised test sets focused on the translation of these two conjunctions. The test sets contain source languages that do not distinguish different variants of the given conjunction, whereas the target languages do. In total, we evaluate the conjunction “but” on 20 translation outputs, and the conjunction “and” on 10. All machine translation systems almost perfectly recognise one variant of the target conjunction, especially for the source conjunction “but”. The other target variant, however, represents a challenge for machine translation systems, with accuracy varying from 50% to 95% for “but” and from 20% to 57% for “and”. The major error for all systems is replacing the correct target variant with the opposite one

Low-resource machine translation using MATREX: The DCU machine translation system for IWSLT 2009

In this paper, we give a description of the Machine Translation (MT) system developed at DCU that was used for our fourth participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2009). Two techniques are deployed in our system in order to improve the translation quality in a low-resource scenario. The first technique is to use multiple segmentations in MT training and to utilise word lattices in decoding stage. The second technique is used to select the optimal training data that can be used to build MT systems. In this year’s participation, we use three different prototype SMT systems, and the output from each system are combined using standard system combination method. Our system is the top system for Chinese–English CHALLENGE task in terms of BLEU score