The Decades Progress on Code-Switching Research in NLP : A Systematic Survey on Trends and Challenges (2023)

Abstract: Code-Switching, a common phenomenon in written text and conversation, has been studied over decades by the natural language processing (NLP) research community. Initially, code-switching is intensively explored by leveraging linguistic theories and, currently, more machine-learning oriented approaches to develop models. We introduce a comprehensive systematic survey on code-switching research in natural language processing to understand the progress of the past decades and conceptualize the challenges and tasks on the code-switching topic. Finally, we summarize the trends and findings and conclude with a discussion for future direction and open questions for further investigation.

## introduction code-switching is the linguistic phenomenon where multilingual speakers use more than one language in the same conversation @xcite . the fragment of the worldwide population that can be considered multilingual, i.e., speaks more than one language, far outnumbers monolingual speakers @xcite . this alone makes a compelling argument for developing nlp technology that can successfully process code-switched (csw) data. however, it was not until the last couple of years that csw-related research became more popular @xcite @xcite , and this increased interest has been motivated to a large extent by: 1) the need to process social media data. before the proliferation of social media platforms, it was more common to observe code-switching in spoken language and not so much in written language. this is not the case anymore, as multilingual users tend to combine the languages they speak on social media; 2) the increasing release of voice-operated devices. now that smart assistants * the work was done while at bloomberg. are becoming more and more accessible, we have started to realize that assuming users will interact with nlp technology as monolingual speakers is very restrictive and does not fulfill the needs of real-world users. multilingual speakers also prefer to interact with machines in a csw manner @xcite . we show quantitative evidence of the upward trend for csw-related research in figure 1 . in this paper, we present the first large-scale comprehensive survey on csw nlp research in a structured manner by collecting more than 400 papers published on open repositories, such as the acl anthology and isca proceedings (see §2). we manually coded these papers to collect coarseand fine-grained information (see §2.1) on csw research in nlp that includes languages covered (see §3), nlp tasks that have been explored, and new and emerging trends (see §4). in addition, motivated by the fact that fields like linguistics, socio-linguistics, and related fields, have studied the area of nlp for csw data is thriving, covering an increasing number of language combinations and tasks, and it is clearly advancing from a niche field to a common research topic, thus making our comprehensive survey timely. we expect the survey to provide valuable information to researchers new to the field and motivate more research from researchers already engaging in nlp for csw data. ## exploring open proceedings to develop a holistic understanding of the trends and advances in csw nlp research, we collect research papers on csw from the acl anthology and isca proceedings. we focus on these two sources because they encompass the top venues for publishing in speech and language processing in our field. in addition, we also look into personal repositories from researchers in the community that contains a curated list of csw-related papers. we discuss below the search process for each venue. ## language diversity here, we show the languages covered in the csw resources. while focusing on the csw phenomenon increases diversity of nlp technology, as we will see in this section, future efforts are needed to provide significant coverage of the most common csw language combinations worldwide. ## tasks and datasets in this section, we summarize our findings, focusing on the csw tasks and datasets. dataset source paper category ## conclusion we present a comprehensive systematic survey on code-switching research in natural language processing to explore the progress of the past decades and understand the existing challenges and tasks in the literature. we summarize the trends and findings and conclude with a discussion for future direction and open questions for further investigation. we hope this survey can encourage and lead nlp researchers in a better direction on code-switching research.