Improving Machine Translation Formality Control with Weakly-Labelled Data Augmentation and Post Editing Strategies (2022)

Abstract: This paper describes Amazon Alexa AI’s implementation for the IWSLT 2022 shared task on formality control. We focus on the unconstrained and supervised task for en→hi (Hindi) and en→ja (Japanese) pairs where very limited formality annotated data is available. We propose three simple yet effective post editing strategies namely, T-V conversion, utilizing a verb conjugator and seq2seq models in order to rewrite the translated phrases into formal or informal language. Considering nuances for formality and informality in different languages, our analysis shows that a language-specific post editing strategy achieves the best performance. To address the unique challenge of limited formality annotations, we further develop a formality classifier to perform weakly labelled data augmentation which automatically generates synthetic formality labels from large parallel corpus. Empirical results on the IWSLT formality testset have shown that proposed system achieved significant improvements in terms of formality accuracy while retaining BLEU score on-par with baseline.

## introduction although neural machine translation (nmt) models have achieved state-of-the-art results with high bleu scores 1 , given a language pair, they are trained on generic parallel corpora that are extracted from various open source datasets such as the europarl corpus @xcite . these datasets make an implicit assumption that there is a single translation in the target language to a sentence from the source language. but the style of the language generated, through which meaning is conveyed, is also important @xcite . thus, there is a need to control certain attributes of the text generated in a target language such as politeness or formality. *equal contribution. 1 http://nlpprogress.com/english/machine_translation. html in this paper, we present our system for the iwslt 2022 formality control task for machine translation. 2 we focus on the unconstrained and supervised scenario for @xmath0. in the proposed system, we explore post editing strategies that correct or alter textual formality once the translation has been completed. post editing strategies can be language specific or language agnostic. we propose three strategies, t-v conversion (deterministically converting the informal or t-form of a pronoun to its corresponding formal or v-form) , verb conjugation, and a seq2seq model that learns to transform input text to be of a formal or informal nature. the t-v conversion and verb conjugation are language-specific strategies that are applied to @xmath1, and @xmath2. these two methods are compared against an alternative seq2seq model @xcite that is language agnostic. we show that compared to a baseline translation model provided in task, a finetuned mbart model @xcite with language-specific rule-based post editing significantly improved the baseline model performance and achieved the best formality control accuracy and bleu score. a unique challenge in this iwslt formality shared task is data sparsity -only few hundred formality annotated samples are available for finetuning the formality controlled nmt model. therefore, we further devise a data augmentation method, utilizing linguistic cues to automatically annotate a small seed set of target (i.e., hindi and japanese) texts with formality labels. then the seed set is utilized to train a multilingual text formality classifier that can further mine massive parallel corpus to find extra formality annotated data. we found such weakly-labeled data augmentation strategy significantly improved @xmath3. the paper is organized into the following secto tions: §2 describes each method, §3 shows the performance of each method and language it is applied to and §4 discusses the prior work on formality. ## background the task of controlling formality in the output of machine translation has drawn much attention in recent mt architectures. earlier approaches are rule-based systems where non-linguistic information such as speaker profile and gender information is used to personalized mt with gender/speakerspecific data @xcite . more recently, @xcite coined the term formality sensitive machine translation (fsmt), and proposed lexical formality models to control the level of formality of mt output by selecting phrases of that are most similar to a desired formality level from the k-best list during decoding. alternatively, a popular formality control approach is by leveraging side constraints in nmt where a style tag (e.g., <@xmath4, and the nmt model is forced to "pay attention to" these style tags during translation @xcite . formality control for machine translation is closely related to formality transfer (ft), which is the task of automatically transforming text in one formality style (e.g., "informal") into another (e.g., polite) @xcite . the ft task usually takes a seq2seq-like approach @xcite given parallel corpus such as grammarly's yahoo answers formality corpus (gyafc) @xcite . these ft models are often applied as a rewriting mechanism after the mt outputs are generated. recently, @xcite proposed a novel multi-task model that jointly perform ft and fsmt. honorifics based post editing approaches have also been widely deployed for formality control tasks. a widespread instance of using honorifics to determine register is the grammatical t-v distinction @xcite , distinguishing between the informal (latin tu) and the formal (latin vos). alternatively, verb conjugation combined with syntactic parsing has been used to alter the inflection of the main verb of the sentence to achieve multiple levels of formality @xcite . ## conclusion in this paper, we target improving the machine translation formality control performance given limited formality annotated training data. we explored three different strategies including rule-based post editing, seq2seq point networks, and formality classifier-based augmentation. we found that data augmentation using formality classifier significantly improved formality accuracy on @xmath5. we also found that post editing strategies on top of finetuned mbart models are simple and effective ways to improve the formality control performance. results on the iwslt testset have indicated performance improvements in terms of formality accuracy in both @xmath6.