Lost in Translationese? Reducing Translation Effect Using A bstract M eaning R epresentation (2024)

Abstract: Translated texts bear several hallmarks distinct from texts originating in the language (“translationese”). Though individual translated texts are often fluent and preserve meaning, at a large scale, translated texts have statistical tendencies which distinguish them from text originally written in the language and can affect model performance. We frame the novel task of translationese reduction and hypothesize that Abstract Meaning Representation (AMR), a graph-based semantic representation which abstracts away from the surface form, can be used as an interlingua to reduce the amount of translationese in translated texts. By parsing English translations into an AMR and then generating text from that AMR, the result more closely resembles originally English text across three quantitative macro-level measures, without severely compromising fluency or adequacy. We compare our AMR-based approach against three other techniques based on machine translation or paraphrase generation. This work represents the first approach to reducing translationese in text and highlights the promise of AMR, given that our AMR-based approach outperforms more computationally intensive methods.

## introduction the term translationese @xcite describes the features unique to translated texts: the specific syntactic and semantic patterns found in human translations @xcite . when the presence of translationese is not addressed in training or test sets, evaluation scores can be overinflated @xcite @xcite , model performance can be impacted @xcite , or system-generated output can be dispreferred by humans @xcite . however, if used correctly, actively leveraging translated texts in language model training can lead to improved performance in machine translation systems @xcite @xcite . original translation: now, however, he is to go before the courts once more because the public prosecutor is appealing. ## background on translationese translated and non-translated text (originally written in that language) exhibit various differences referred to as "translationese" @xcite . translated text is often less lexically rich, has simpler constructions, exhibits explicitation, and demonstrates specific lexical and word order choices @xcite . an example exhibiting translationese can be seen at the top of figure 1 . the presence of translationese is not necessarily indicative of a low-quality trans-lation @xcite , and prior work has shown that human raters are not able to accurately predict whether text is translated or not @xcite . two basic types of translationese include: (1) interference from the source, such as the presence of syntactic patterns typical of the source language @xcite , and (2) over-normalizing to the target language, for example not translating abnormalities seen in the source text. the patterns and characteristics of translationese vary by mode and register, most notably if the translation is written or spoken @xcite ; translationese found in human translations versus machine translations (mt) also exhibit different characteristics @xcite . related work has also considered the impact and causes of translationese via investigating the algorithmic biases which lead to translationese in mt @xcite , avoiding the influence of translationese in training and testing by means of translationese classifiers and zero-shot multilingual mt @xcite , and exploring the utility of word-by-word glosses in producing fluent translations @xcite . prior work has developed automatic classifiers of translationese, which detect whether the text exhibits translationese or not @xcite @xcite . a couple of studies have sought to counteract the effects of translationese. contemporaneously to the present work, @xcite evaluated translationese classifier accuracy before and after applying style transfer to translated texts. in a similar vein, dutta @xcite removed translationese implicitly encoded in vector embeddings (but did not produce a reducedtranslationese version of the translated text). our work is novel in that we (1) frame the task of translationese reduction as one which reduces the statistical patterns of translationese, while preserving meaning and fluency, (2) introduce three methods of translationese reduction, and (3) demonstrate on both quantitative and qualitative metrics that our amr-based approach succeeds at reducing the presence of translationese. ## translationese reduction we undertake this task of automatic translationese reduction for english, where the input is a sentence 2 that has been translated into english and the output is a paraphrase that better resembles a sentence that originated in english. we do not assume access to the source sentence that was translated, or even to the source language. we formulate the task of translationese reduction by proposing automatic metrics for diminishing the hallmarks of translationese, informed by prior work documenting the notable features of translated texts. importantly, fluency and adequacy must be preserved in the task of translationese reduction, as conveying the same meaning is paramount. thus, the reduction of translationese hallmarks across various automatic metrics may not come at the cost of adequacy or fluency, and any viable method for translationese reduction needs to maintain these aspects of the text while reducing features of translationese. in this work, we approach translationese reduction by first mapping the translated english into a meaning representation in order to abstract away from superficial aspects of expression that may be artifacts of the translation process. this meaning representation is intended as an intermediary, or "interlingua," between the two "dialects" of english: translationese and originally english text. for example, in figure 1 , we see that we start with a translation, parse the text into an amr graph, and generate from that amr graph a sentence more like original english text. ## methods first, in §4.1, we introduce the data that we use for our experiments. next, in the three subsections that follow, we outline the three approaches we develop to take on our task of translationese reduction: one using paraphrase generation models ( §4.2); one using machine translation ( §4.3); and the third approach using amr as an interlingua ( §4.4). foot_2 ## measuring translationese prior work has established several statistical properties of translated text @xcite . measures known to distinguish translations from nontranslations include: (1) type-token ratio (ttr), (2) the presence of cohesive markers, and (3) unigram bag-of-part-of-speech (pos) tags. note that while the metrics we apply here are informed by prior work both in natural language processing and translation studies, these metrics show a partial picture of the range of statistical patterns observed in translated texts. these are not "translation universals," per se, so much as they are statistical tendencies (jimenez-crespo, 2023) observed in prior work on features of translated texts. we compare system outputs on these metrics, using the original translations as a baseline, to assess whether each system successfully mitigates the observed presence of translationese. in each subsection, we detail the metric as well as the results for each approach with that metric. ## evaluation of fluency and adequacy having established that macro-level indications of translationese are lessened by using either the bart-based paraphrase model or amr as an interlingua, we now examine the quality of the generated sentences through the lenses of fluency and adequacy/meaning preservation. we report automatic metrics as well as results of a human evaluation study. ## conclusion in this work, we investigated the task of translationese reduction and introduced three methods for this task. our automatic metrics of translationese indicate that the task of translationese reduction is complicated, because we want translationese to be reduced without sacrificing fluency or adequacy (this tradeoff is discussed in §6.3). overall, we find that using amr as an interlingua aids in translationese reduction. by contrast, a bart-based paraphrase model is even more effective at reducing translationese, but dramatically over-summarizes, severely harming adequacy and fluency. the t5based paraphrase model and mt back-translation approach do not show promise for this task. our findings suggest that translationese reduction could be performed as an additional step after translating to make the text more like originally english text, and provides further indication that amr can serve as an interlingua for a range of tasks which require abstracting away from specific language features (cf. @xcite @xcite .