Towards human linguistic machine translation evaluation

When evaluating machine translation outputs, linguistics is usually taken into account implicitly. Annotators have to decide whether a sentence is better than another or not, using, for example, adequacy and fluency criteria or, as recently proposed, editing the translation output so that it has the same meaning as a reference translation, and it is understandable. Therefore, the important fields of linguistics of meaning (semantics) and grammar (syntax) are indirectly considered. In this study, we propose to go one step further towards a linguistic human evaluation. The idea is to introduce linguistics implicitly by formulating precise guidelines. These guidelines strictly mark the difference between the sub-fields of linguistics such as: morphology, syntax, semantics, and orthography. We show our guidelines have a high inter-annotation agreement and wide-error coverage. Additionally, we examine how the linguistic human evaluation data correlate with: among different types of machine translation systems (rule and statistical-based); and with adequacy and fluency.Peer ReviewedPostprint (published version

Segment-based interactive-predictive machine translation

[EN] Machine translation systems require human revision to obtain high-quality translations. Interactive methods provide an efficient human¿computer collaboration, notably increasing productivity. Recently, new interactive protocols have been proposed, seeking for a more effective user interaction with the system. In this work, we present one of these new protocols, which allows the user to validate all correct word sequences in a translation hypothesis. Thus, the left-to-right barrier from most of the existing protocols is broken. We compare this protocol against the classical prefix-based approach, obtaining a significant reduction of the user effort in a simulated environment. Additionally, we experiment with the use of confidence measures to select the word the user should correct at each iteration, reaching the conclusion that the order in which words are corrected does not affect the overall effort.The research leading to these results has received funding from the Ministerio de Economia y Competitividad (MINECO) under Project CoMUN-HaT (Grant Agreement TIN2015-70924-C2-1-R), and Generalitat Valenciana under Project ALMAMATER (Ggrant Agreement PROMETEOII/2014/030).Domingo-Ballester, M.; Peris-Abril, Á.; Casacuberta Nolla, F. (2017). Segment-based interactive-predictive machine translation. Machine Translation. 31(4):163-185. https://doi.org/10.1007/s10590-017-9213-3S163185314Alabau V, Bonk R, Buck C, Carl M, Casacuberta F, García-Martínez M, González-Rubio J, Koehn P, Leiva LA, Mesa-Lao B, Ortiz-Martínez D, Saint-Amand H, Sanchis-Trilles G, Tsoukala C (2013) CASMACAT: an open source workbench for advanced computer aided translation. 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Systemic Functional Linguistics as a tool for translation teaching: towards a meaningful practice

This paper focuses on the centrality of meaning in the practice of translation. Since this major concern is also shared by Systemic Functional Linguistics (Halliday 1994; Halliday & Matthiessen 2004), which considers language a meaning making resource, it is argued that such an approach could serve as a helpful tool for translator education and training. After a theoretical first part, where the relevance of Systemic Functional Linguistics to the activity of translating is discussed and a cursory sketch of its key notions is outlined, the paper moves on to present illustrative segments from a small selection of English sample texts and of their translation into Italian. Dealing with different text types, and drawing on authentic teaching assignments, some lexicogrammatical features are analysed in order to identify the multidimensional meanings being realized. Special focus is on modality, ideational grammatical metaphor, thematic progression and also on appraisal systems, a model for evaluation recently developed within the framework of Hallidayan linguistics (Martin & White 2005). The empirical examples are offered to show that a based on this perspective might represent for the translator an ideal “set of resources for describing, interpreting and making meaning” (Butt et al. 2000: 3)

Viewpoint in translation of academic writing : an illustrative case study

This article employs the concept of viewpoint, also referred to as point of view or stance, to offer a short case study of semantic shifts in the translation of academic writing. Drawing on a model of the concept developed specifically for research into the subjective aspects of academic prose, the study seeks to show what viewpoint shifts can occur in translation, based on an analysis of a Cognitive Linguistics monograph translated from English into Polish. The examples, supplemented with English back-translation glosses, illustrate several types of viewpoint shifts taking place in translation, such as increasing or decreasing the author’s commitment to a claim, the removal of author emphasis from the text, and shifts from implicit to explicit author mention. Given that academic discourse has ceased to be regarded as objective description of facts, and based on the assumption that the linguistic resources connected with hedging, evaluation and (avoidance of) self-mention are consciously deployed by authors of academic texts, it is suggested that viewpoint phenomena may represent a valuable research area for the strand of translation studies concerned with academic writing

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

An analysis of the translation of vocabulary lists in textbooks for teaching Chinese as a foreign language (TCFL)

Recent research in the Teaching Chinese as a Foreign Language (TCFL) field has focused on the pedagogical perspectives underlying TCFL textbooks and their compilation. With the increasing interaction between China and other countries in global contexts such as culture, economics and commerce, there is a great need to expand research regarding all areas and issues within TCFL, especially in the important area of vocabulary and its translation in TCFL textbooks (Tsung and Cruickshank, 2011). This research investigates a range of translation problems related to the accuracy of the vocabulary lists featured in 12 selected representative TCFL textbooks for teaching Chinese as a foreign language. This thesis presents findings from three triangulation cases (questionnaire survey, corpus research, and assessment test) involving two different groups of participants (e.g. Chinese teachers who completed the questionnaire survey and Chinese undergraduates majoring in English who underwent the assessment test). The contribution of this study is as follows: 1) I conduct a series of empirical evidence based on the viewpoints of practitioners regarding the identified translation problems to fill the gap that there are more descriptive and pedagogical works in the vocabulary translation of TCFL textbooks; 2) I adopt functional equivalence theory of translation and linguistics–based approaches (semantic, pragmatic and grammatical perspectives) to establish a theoretical framework which provides a flexible way of analysing translation and enables the original meanings of Chinese words to be analysed through various perspectives, especially for Chinese and English vocabulary analysis and translation; 3) I draw on translation quality evaluation theory to generate a translation quality evaluation framework which can serve as a reference point for other translation evaluation work regarding vocabulary conducted during other relevant studies; 4) I demonstrate that the majority of translation problems gathered from the selected TCFL textbooks were found at the preliminary level and in the content word class which have much practical relevance and research value for the pedagogical purpose of vocabulary teaching and translation; and 5) I build up a specific parallel corpus with passages and vocabulary lists of the selected TCFL textbooks

A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic