Evaluating conjunction disambiguation on English-to-German and French-to-German WMT 2019 translation hypotheses

We present a test set for evaluating an MT system’s capability to translate ambiguous conjunctions depending on the sentence structure. We concentrate on the English conjunction ”but” and its French equivalent ”mais” which can be translated into two different German conjunctions. We evaluate all English-to-German and French-to-German submissions to the WMT 2019 shared translation task. The evaluation is done mainly automatically, with additional fast manual inspection of unclear cases. All systems almost perfectly recognise the ta-get conjunction ”aber”, whereas accuracies fo rthe other target conjunction ”sondern” range from 78% to 97%, and the errors are mostly caused by replacing it with the alternative cojjunction ”aber”. The best performing system for both language pairs is a multilingual Transformer TartuNLP system trained on all WMT2019 language pairs which use the Latin script, indicating that the multilingual approach is beneficial for conjunction disambiguation. As for other system features, such as using synthetic back-translated data, context-aware, hybrid, etc., no particular (dis)advantages can be observed. Qualitative manual inspection of translation hypotheses shown that highly ranked systems generally produce translations with high adequacy and fluency, meaning that these systems are not only capable of capturing the right conjunction whereas the rest of the translation hypothesis is poor. On the other hand, the low ranked systems generally exhibit lower fluency and poor adequacy

Automated text simplification as a preprocessing step for machine translation into an under-resourced language

In this work, we investigate the possibility of using fully automatic text simplification system on the English source in machine translation (MT) for improving its translation into an under-resourced language. We use the state-of-the-art automatic text simplification (ATS) system for lexically and syntactically simplifying source sentences, which are then translated with two state-of-the-art English-to-Serbian MT systems, the phrase-based MT (PBMT) and the neural MT (NMT). We explore three different scenarios for using the ATS in MT: (1) using the raw output of the ATS; (2) automatically filtering out the sentences with low grammaticality and meaning preservation scores; and (3) performing a minimal manual correction of the ATS output. Our results show improvement in fluency of the translation regardless of the chosen scenario, and difference in success of the three scenarios depending on the MT approach used (PBMT or NMT) with regards to improving translation fluency and post-editing effort

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

On context span needed for machine translation evaluation

Despite increasing efforts to improve evaluation of machine translation (MT) by going beyond the sentence level to the document level, the definition of what exactly constitutes a ``document level'' is still not clear. This work deals with the context span necessary for a more reliable MT evaluation. We report results from a series of surveys involving three domains and 18 target languages designed to identify the necessary context span as well as issues related to it. Our findings indicate that, despite the fact that some issues and spans are strongly dependent on domain and on the target language, a number of common patterns can be observed so that general guidelines for context-aware MT evaluation can be drawn

QRev: Machine translation of user reviews: what influences the translation quality?

This project aims to identify the important aspects of translation quality of user reviews which will represent a starting point for developing better automatic MT metrics and challenge test sets, and will be also helpful for developing MT systems for this genre. We work on two types of reviews: Amazon products and IMDb movies, written in English and translated into two closely related target languages, Croatian and Serbian

On the differences between human translations

Many studies have confirmed that translated texts exhibit different features than texts originally written in the given language. This work explores texts translated by different translators taking into account expertise and native language. A set of computational analyses was conducted on three language pairs, English-Croatian, German-French and English-Finnish, and the results show that each of the factors has certain influence on the features of the translated texts, especially on sentence length and lexical richness. The results also indicate that for translations used for machine translation evaluation, it is important to specify these factors, especially when comparing machine translation quality with human translation quality

Improving machine translation of English relative clauses with automatic text simplification

This article explores the use of automatic sentence simplification as a preprocessing step in neural machine translation of English relative clauses into grammatically complex languages. Our experiments on English-to-Serbian and English to-German translation show that this approach can reduce technical post-editing effort (number of post-edit operations) to obtain correct translation. We find that larger improvements can be achieved for more complex target languages, as well as for MT systems with lower overall performance. The improvements mainly originate from correctly simplified sentences with relatively complex structure, while simpler structures are already translated sufficiently well using the original source sentences

Extracting correctly aligned segments from unclean parallel data using character n-gram matching

Training of Neural Machine Translation systems is a time- and resource-demanding task, especially when large amounts of parallel texts are used. In addition, it is sensitive to unclean parallel data. In this work, we explore a data cleaning method based on character n-gram matching. The method is particularly convenient for closely related language since the n-gram matching scores can be calculated directly on the source and the target parts of the training corpus. For more distant languages, a translation step is needed and then the MT output is compared with the corresponding original part. We show that the proposed method not only reduces the amount of training corpus, but also can increase the system’s performance

Automatic error classification with multiple error labels

Although automatic classification of machine translation errors still cannot provide the same detailed granularity as manual error classification, it is an important task which enables estimation of translation errors and better understanding of the analysed MT system, in a short time and on a large scale. State-of-the-art methods use hard decisions to assign single error labels to each word. This work presents first results of a new error classification method, which assigns multiple error labels to each word. We assign fractional counts for each label, which can be interpreted as a confidence for the label. Our method generates sensible multi-error suggestions, and improves the correlation between manual and automatic error distributions

Agree to disagree: analysis of Inter-annotator disagreements in human evaluation of machine translation output

This work describes an analysis of inter-annotator disagreements in human evaluation of machine translation output. The errors in the analysed texts were marked by multiple annotators under guidance of different quality criteria: adequacy, comprehension, and an unspecified generic mixture of adequacy and fluency. Our results show that different criteria result in different disagreements, and indicate that a clear definition of quality criterion can improve the inter-annotator agreement. Furthermore, our results show that for certain linguistic phenomena which are not limited to one or two words (such as word ambiguity or gender) but span over several words or even entire phrases (such as negation or relative clause), disagreements do not necessarily represent ``errors'' or ``noise'' but are rather inherent to the evaluation process. %These disagreements are caused by differences in error perception and/or the fact that there is no single correct translation of a text so that multiple solutions are possible. On the other hand, for some other phenomena (such as omission or verb forms) agreement can be easily improved by providing more precise and detailed instructions to the evaluators