Translation of Multifaceted Data without Re-Training of Machine Translation Systems (2024)

Abstract: Translating major language resources to build minor language resources becomes a widely-used approach. Particularly in translating complex data points composed of multiple components, it is common to translate each component separately. However, we argue that this practice often overlooks the interrelation between components within the same data point. To address this limitation, we propose a novel MT pipeline that considers the intra-data relation. in implementing MT for training data. In our MT pipeline, all the components in a data point are concatenated to form a single translation sequence and subsequently reconstructed to the data components after translation. We introduce a Catalyst Statement (CS) to enhance the intra-data relation, and Indicator Token (IT) to assist the decomposition of a translated sequence into its respective data components. Through our approach, we have achieved a considerable improvement in translation quality itself, along with its effectiveness as training data. Compared with the conventional approach that translates each data component separately, our method yields better training data that enhances the performance of the trained model by 2.690 points for the web page ranking (WPR) task, and 0.845 for the question generation (QG) task in the XGLUE benchmark.

## introduction machine translation (mt) has been developed to aid human-level utilization, with its primary focus on the accurate translation of any given sequence (i.e., ensuring semantic preservation and syntactic fluency) @xcite @xcite . as previous mt systems have demonstrated relatively low performance @xcite , their translation outputs are hardly utilized as another data source. with the ongoing advancement of mt research, the translation performance of mt systems becomes comparable to the expert humanlevel @xcite , and subsequently several attempts have recently emerged to utilize mt system for the data translation process @xcite @xcite @xcite @xcite . particularly, several non-english datasets are vig-orously being constructed by translating english datasets @xcite @xcite . in applying mt to data translation, one concern we raise is the conservation of the intra-data relation during mt process. depending on the task composition, a single data point may comprise multiple components. for example, each data point of natural language inference (nli) task comprises three components; namely, the hypothesis and the premise along with one label. in translating such multifaceted components, we encounter dilemmas in determining the input unit, considering mt systems generally take a singular sequence. in dealing with this situation, current research predominantly translates each individual data component separately @xcite . however, we argue that such data translation approaches may not yield optimal results, as the interrelation among components in the same data can easily be disregarded. as shown in figure 1 -(a), the translated pair may not accurately maintain the original label, despite the absence of any error in their respective translations. this can further derive performance degradation of the model trained with these translated datasets, as the purpose of the task is generally represented within the interrelation among data components. theoretically, this issue can partially be alleviated by simply concatenating all the components in a single sequence for translation. then in translating each component, mt system can refer the semantics of other components in the same sequence. however, in this case, mt system often merges all the components and generates an inseparable result to form a natural context. as shown in figure 1 -(b), this presents challenges in distinguishing data components from the translated sequence. upon these considerations, we propose a simple yet effective mt pipeline for the data translation that can be applied to any mt systems without further re-training. in particular, we propose a relation-aware translation that strategically concatenates multifaceted components into a singular sequence, as in figure 2 . especially in concatenating data components, we discern the following two aspects: (i) the inter-relation between components should be considered in a concatenated sequence. (ii) translated sequence should be reversible (i.e. can explicitly be converted to the translated data components). to attain these objectives, we introduce indicator token (it) and catalyst statement (cs). it is basically designed to distinguish the location of each data component and help conversion of the translated sequence into the translated components. cs is devised to specify the definite relation between each component in the concatenated sequence for enhancing the interrelation between components. constructed sample is shown in figure 1-(c ). for validation, we select multilingual benchmark tasks in which the maintenance of the interrelation among data components plays a critical role. specifically, we adopt the xnli dataset @xcite and select two tasks in an xglue benchmark @xcite : web page ranking (wpr) and question generation (qg). we construct training data for up to five languages (german, french, chinese, hindi, and vietnamese) by translating the english dataset existing within each dataset. subsequently, by evaluating the performance of the models trained on each translated data, we estimate the validity of each data translation strategy. notably, our proposed data translation pipeline demonstrates a more effective strategy to attain high-quality training data, compared to the individual translation of each data component. ## related works attempts to construct training data with mt systems can broadly be divided into two major approaches. the first approach aims to construct a task-specific mt system by training with any corpus specially constructed for reaching intended goal @xcite @xcite @xcite . for instance, @xcite trained a new translation model with a manually curated domain-specific dataset, then made a polish training corpus for virtual assistant by translating english dataset. however, these attempts encounter difficulties in utilizing newly released assets. in contrast, the second approach covers attempts to use publicly released nmt models without any modification, in constructing datasets via translation @xcite @xcite @xcite . representatively, commercialized nmt systems such as deepl foot_1 @xcite ., single data point input sentence source dataset extract label sentence data to sequence : concatenate data into a single sentence [ cs ] [ it ] input sentence [ it ] label sentence machine translation [ cs (tr) ] [ it ] input sentence (tr) [ it ] label sentence (tr) sequence to data 2023) or google translator 3 @xcite , along with publicly released nmt models @xcite ) are adopted to construct multilingual training datasets @xcite . however, prior approaches using existing assets with modification have encountered limitations in performing accurate data translation considering the interrelation among components comprising each data. considering these attempts and their challenges, we focus on establishing an easy-to-implement pipeline for data translation that utilizes mt systems without model modification and takes into account intra-data relation. ## conclusion this study explored challenges encountered when implementing data translation through mt frameworks. we highlighted that individual translation of each data component neglects their interrelations, leading to a compromise in data quality. while composing a singular sequence by concatenating all the components theoretically can alleviate this, it also introduces the limitation of the inability to restore data components from the translated sequence. as a solution, we introduced a relationconsidering translation pipeline that integrates it and cs. this approach led to a substantial enhancement in the quality of training data as opposed to separate component translation. our empirical findings underscored the paramount importance of inter-component relation in data translation, emphasizing that considering this relation can facilitate high-level data translations. this progression lays a foundation for future data translation research.