Inria- ALMA na CH at WMT 2022: Does Transcription Help Cross-Script Machine Translation? (2022)

Abstract: This paper describes the Inria ALMAnaCH team submission to the WMT 2022 general translation shared task. Participating in the language directions cs,ru,uk→en and cs↔uk, we experiment with the use of a dedicated Latin-script transcription convention aimed at representing all Slavic languages involved in a way that maximises character- and word-level correspondences between them as well as with the English language. Our hypothesis was that bringing the source and target language closer could have a positive impact on machine translation results. We provide multiple comparisons, including bilingual and multilingual baselines, with and without transcription. Initial results indicate that the transcription strategy was not successful, resulting in lower results than baselines. We nevertheless submitted our multilingual, transcribed models as our primary systems, and in this paper provide some indications as to why we got these negative results.

## introduction this paper describes the inria almanach team submission to the wmt 2022 general translation shared task. we chose to explore the language directions {cs,ru,uk}↔en and cs↔uk in order to concentrate on the slavic language family. due to some experimental problems that impacted the into-slavic directions most heavily, we only submitted {cs,ru,uk}→en and cs↔uk language directions, but we present all results we obtained here. a major area of interest in machine translation (mt) research is transfer between languages, particularly related ones and for lesser resourced languages @xcite . one way of encouraging transfer is to train multilingual models, whereby several language directions are trained simultaneously, often sharing some @xcite or all model parameters (ha et al., * contributions made whilst at inria. 2016; @xcite , with the hope that similarities between the languages can boost performance, particularly for the lower-resourced languages. to encourage lexical sharing and therefore the transfer capacity of such models, joint subword segmentation models @xcite and mt vocabularies are often used @xcite , and techniques such as phonetisation and transliteration/transcription can be applied to texts in a bid to overcome differences in writing systems and spelling @xcite @xcite . in our submission to the wmt 2022 general translation shared task, we experimented with multilingual models and the use of customised transcription into a common writing system designed to maximise lexical sharing, similar to the one used in @xcite . we choose to work with the language directions involving slavic languages, that is {cs,ru,uk}↔en and cs↔uk. we find that our transcription method unfortunately leads to degraded results, likely a consequence of errors being injected and notably the necessity to apply a learned detranscription model as a post-processing step for into-slavic language directions. our multilingual models achieved largely inferior results to our bilingual baseline models for the same number of parameters, showing that multilingual transfer cannot compensated for sharing the vocabulary over a larger number of languages. transcribing the languages in the multilingual setup results narrows the gap slightly, but the results remain lower than the bilingual baselines. we nevertheless decided to submit our multilingual models with common-slavic transcription rather than our superior baseline results in the full knowledge that these results would not achieve the best results in the shared task. ## related work there has been a considerable body of work in mt dedicated to multilingual models, whereby several language directions are trained simultaneously, with different degrees of parameter sharing, ranging from separate encoders and decoders @xcite to the sharing of a single encoder and a single decoder for all languages with a single shared vocabulary @xcite @xcite . as well as being practical by providing a single mt model that can be used for multiple directions, the models have the advantage of aiding the representations of lower-resourced languages, particularly if related, higher-resourced languages are also included in training @xcite @xcite . in addition to approaches such as joint subword segmentation models @xcite and the use of a joint vocabulary for all languages @xcite , strategies to encourage more lexical sharing have also been explored in order to overcome surface differences introduced by orthographic conventions, notably phonetisation @xcite @xcite and transliteration @xcite @xcite . these approaches can be particularly useful for borrowings and for proper nouns, which can be made to be identical (or near-identical) across languages once transliteration has been applied. transliteration is the mapping of one writing system to another, and therefore is relevant when languages are written in different scripts (e.g. latin, cyrillic, devanagari, etc.). in particular for related languages, it can be interesting to apply transliterabaseline systems. due to human error, these submitted models perform less well than the results presented in this paper, as described in section 5.3. tion in order to exploit the fact that many words can be made to be similar on the surface once transliteration has been applied. much of the work that has explored transliteration for mt has focused on indian languages, for which the mapping between scripts is relatively straightforward @xcite @xcite , but there has also been research on other language families @xcite , including slavic languages @xcite . in our systems, we follow a similar approach to test whether a form of transliteration that maximises lexical overlap between slavic languages could help translation in a multilingual setup, even in the relatively highresource scenario provided by the shared task. ## multilingual slavic models with transcription building on the previous work on multilingual mt and on transliteration to encourage lexical sharing, we propose multilingual models with a custom linguistically motivated transcription scheme for translation between english and the slavic languages czech (cs), ukrainian (uk) and russian (ru). ## data we developed systems for four of the several language combinations taken into account for the general translation task. they are {cs,ru,uk}↔en and cs↔uk. we took part in the challenge under its constrained track, using only a portion of the data made available for the task. the following sections describe the data we used and how we processed and filtered it. we present the data sizes and their corresponding sources in ## experiments and training we submitted three categories of nmt systems: (i) the baseline bilingual translation models for each of the four language pairs in their original scripts, (ii) a multilingual model with common-slavic transcription for {cs,uk,ru}→en, and (iii) a bilingual model with common-slavic transcription for cs↔uk. below, we provide details of these submitted systems, as well as the additional systems developed before and after the task's deadline. ## discussion given these disappointing results, it is important to make a first step to understanding why transcription does not help. we therefore look at some additional results concerning the noise that the transcription step might be introducing: (i) the translation results for the detranscription step itself and (ii) comparative results for cs↔uk when transcribing the source, the target or both. detranscription quality we show the results for the detranscription step itself in we also provide results ( the results for cs↔uk when transcribing the source, target or both. the best results when using the original scripts, as can be seen in previous results. however, the results suggest that in some scenarios, it could be better to transcribe just the source rather than to transcribe both source and target. the advantage of this for @xmath0, but without there being extra errors added by the detranscription step. @xcite showed that transcribing could be useful for lower-resource languages, so a possibility here is that the languages are sufficiently high-resource for transcription not to help so much and for the errors introduced in detranscription to outweigh any potential benefits. ## conclusion setting aside the fact that multilingual models provide very inferior results to specific bilingual models for the same number of parameters, our results