A rz E n- ST : A Three-way Speech Translation Corpus for Code-Switched E gyptian A rabic- E nglish (2022)

Abstract: We present our work on collecting ArzEn-ST, a code-switched Egyptian Arabic-English Speech Translation Corpus. This corpus is an extension of the ArzEn speech corpus, which was collected through informal interviews with bilingual speakers. In this work, we collect translations in both directions, monolingual Egyptian Arabic and monolingual English, forming a three-way speech translation corpus. We make the translation guidelines and corpus publicly available. We also report results for baseline systems for machine translation and speech translation tasks. We believe this is a valuable resource that can motivate and facilitate further research studying the code-switching phenomenon from a linguistic perspective and can be used to train and evaluate NLP systems.

## introduction code-switching (csw), defined as the alternation of language in text or speech, is a common linguistic phenomenon in multilingual societies. csw can occur on the boundaries of sentences, words (within the same sentence), or morphemes (within the same word). while the worldwide prevalence of csw has been met with increasing efforts in nlp systems trying to handle such mixed input, data sparsity remains one of the main bottlenecks hindering the development of such systems @xcite . in this paper, we present arzen-st, 1 a speech translation (st) corpus for code-switched egyptian arabic (egy) -english. we extend the arzen egyptian arabic-english csw conversational speech corpus @xcite with translations going to both directions; the primary (egyptian arabic) as well as secondary (english) languages. see figure 1 . this corpus is a valuable resource filling an important gap, given the naturalness and high 1 arz is the iso 639-3 code for egyptian arabic. frequency of csw in it. it can be used for the purpose of linguistic investigations as well as for building and evaluating nlp systems. we provide benchmark baseline results for the tasks of automatic speech recognition (asr), machine translation (mt), and st. we make the translation guidelines and full corpus available, as well as the experiments' scripts and data splits. 2 the paper is organized as follows. in section 2, we provide an overview of previous work done for code-switched asr, mt, and st tasks as well as corpora collection. in section 3, we provide an overview of the arzen speech corpus. in section 4, we elaborate on the translation guidelines used to create the three-way parallel st corpus. finally, in section 5, we report the performance of the asr, mt, and st baseline systems. ## overview of the arzen corpus arzen is a conversational speech corpus that is collected through informal interviews. the interviews were held at the german university in cairo, which is a private university where english is the instruction language. the topics discussed were general topics such as education, work and life experiences, career, technology, personal life, hobbies, and travelling experiences. no instructions were given to participants regarding code-switching; they were not asked to produce nor avoid code-switching. interviews were held with 38 egyptian arabic-english bilingual speakers (61.5% males, 38.5% females), in the age range of 18-35, who are students (55%) and employees (45%) at the university. the speech corpus comprises of 12 hours of speech, containing 6,216 sentences. ## arzenst translation guidelines the transcriptions are translated to monolingual english and monolingual egyptian arabic sentences by human translators. 4 in this section, we discuss the translation guidelines. in general, our decisions are mainly guided by giving a higher priority to fluency over accuracy. we opt for producing as natural as possible outputs that reflect the style of the original sentence. even though we acknowledge that some of our decisions can make the translation task harder for mt systems, our goal is to produce natural translations. the guidelines cover three categories, general translation rules (denoted by gr), conversational speech translation rules (denoted by sr), and code-switching translation rules (denoted by cswr). in ## benchmarking baseline systems in this section, we discuss the asr, mt, and st baseline systems. we describe the experimental setup for each and present the results in ## conclusion and future work code-switching has become a worldwide prevalent phenomenon. this created a need for nlp systems to be able to handle such mixed input. code-switched data is typically scarce, which is evident in the limited number of available corpora for machine translation and speech translation tasks. in this paper, we extend the previously collected arzen speech corpus with translations to both its primary and secondary languages, providing a three-way code-switched egyptian arabic-english speech translation corpus. we have discussed the translation guidelines, particularly with regards to issues arising due to the spontaneous nature of the corpus as well as code switching. we reported benchmark results for baseline asr, mt, and st systems. we make this corpus available to motivate and facilitate further research in this area. for future work, we plan on improving the corpus and using it for code-switching linguistic investigations as well as nlp tasks. with regards to corpus improvements, we plan on adding additional translation references and codafying @xcite the corpus. from a linguistic perspective, having signals from the monolingual arabic and english translations, we plan to further understand why code-switching occurs at the given points. finally, we plan to use this corpus for nlp tasks, working on data augmentation for the purpose of improving machine translation and speech translation.