S inglish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization (2022)

Abstract: Within the natural language processing community, English is by far the most resource-rich language. There is emerging interest in conducting translation via computational approaches to conform its dialects or creole languages back to standard English. This computational approach paves the way to leverage generic English language backbones, which are beneficial for various downstream tasks. However, in practical online communication scenarios, the use of language varieties is often accompanied by noisy user-generated content, making this translation task more challenging. In this work, we introduce a joint paraphrasing task of creole translation and text normalization of Singlish messages, which can shed light on how to process other language varieties and dialects. We formulate the task in three different linguistic dimensions: lexical level normalization, syntactic level editing, and semantic level rewriting. We build an annotated dataset of Singlish-to-Standard English messages, and report performance on a perturbation-resilient sequence-to-sequence model. Experimental results show that the model produces reasonable generation results, and can improve the performance of downstream tasks like stance detection.

## introduction while the development of natural language processing (nlp) has been focused on major languages such as english, chinese, and french, there is emerging research interest in similar languages, varieties, and dialects @xcite . the distinction of language variations generally comes from geographical, historic, communicative settings, and social group dimensions @xcite . in particular, creole languages are formed in conditions when major languages (e.g., english, french) are adopted in another culture or region, and they often mix with existing languages and evolve into other varieties in their own right. such examples include the french-based haitian @xcite , english-based australian kriol @xcite , and colloquial singaporean english (singlish) @xcite . to adopt computational nlp solutions on dialects or creoles, applying existing models trained with major language resources will result in degraded performance, and collecting and annotating sufficient data for task-specified domain adaptation is time-consuming and labor-intensive. similar to studies on multilingual scenarios @xcite , one straightforward and effective approach is to conform the varieties to their base languages by machine translation @xcite , then other nlp systems (e.g., sentiment analysis, information retrieval, reading comprehension) that take base languages as input could be applied. however, it would be challenging to apply this approach to dialects or creoles that include certain deviated grammar and local vocabularies with systems trained on the corresponding standard languages, especially to the under-resourced language varieties where machine translation performance is subpar @xcite . singlish, namely the colloquial singapore english, is used in the daily lives of singaporeans @xcite . despite the obvious attribute of inheriting a large vocabulary base and foundational grammatical rules from english, singlish imports terms and features from regional dialects including mandarin, malay, hokkien, teochew, cantonese, and tamil @xcite , making its lexicon, syntax, and semantics deviate significantly from english @xcite . thus, singlish is an expressive language studded with colorful multicultural slangs yet manifested in an extremely condensed form @xcite . as a result, a person only familiar with american or singlish sentence hhh. i kaypoh meh. i thought both of us also e kaypoh type. like dat i dun ask lor... ## singlish message paraphrasing corpus while unsupervised models show impressive results on tasks like semantic similarity matching by leveraging feature-rich pre-trained backbones @xcite , their performance on language generation is still subpar. to foster data-driven approaches via supervised learning, we construct a human-annotated corpus for the singlish message paraphrasing. the raw singlish messages are extracted from the nus short message service (sms) corpus @xcite , which contains a total of 56k message samples from realworld mobile chats. we choose this resource since their data collection process employs singaporean participants, and the sms conversations cover a wide range of topics. 2 the annotation target is to convert the messages from colloquial singapore english to standard american english, and the humanannotated references are expected to be understandable to non-singaporean high school students. ## conclusions in this paper, we analyzed the representative linguistic features of colloquial singapore english, and proposed a joint task of creole language translation and text normalization. we formulated the