Exploiting Out-of-Domain Data Sources for Dialectal Arabic Statistical Machine Translation
Statistical machine translation for dialectal Arabic is characterized by a lack of data since data acquisition involves the transcription and translation of spoken language. In this study we develop techniques for extracting parallel data for one particular dialect of Arabic (Iraqi Arabic) from out-of-domain corpora in different dialects of Arabic or in Modern Standard Arabic. We compare two different data selection strategies (cross-entropy based and submodular selection) and demonstrate that a very small but highly targeted amount of found data can improve the performance of a baseline machine translation system. We furthermore report on preliminary experiments on using automatically translated speech data as additional training data.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationTranslationSimilar Papers 제목 키워드 기반
Content-Localization based Neural Machine Translation for Informal Dialectal Arabic: Spanish/French to Levantine/Gulf Arabic
Resources in high-resource languages have not been efficiently exploited in low-resource languages to solve language-dependent research problems. Spanish and French are considered high resource languages in which an adeq…
Machine TranslationTranslationExploiting Dialect Identification in Automatic Dialectal Text Normalization
Dialectal Arabic is the primary spoken language used by native Arabic speakers in daily communication. The rise of social media platforms has notably expanded its use as a written language. However, Arabic dialects do no…
Dialect IdentificationText NormalizationCreating Resources for Dialectal Arabic from a Single Annotation: A Case Study on Egyptian and Levantine
Arabic dialects present a special problem for natural language processing because there are few resources, they have no standard orthography, and have not been studied much. However, as more and more written dialectal Ar…
Morphological AnalysisMorphology-aware Word-Segmentation in Dialectal Arabic Adaptation of Neural Machine Translation
Parallel corpora available for building machine translation (MT) models for dialectal Arabic (DA) are rather limited. The scarcity of resources has prompted the use of Modern Standard Arabic (MSA) abundant resources to c…
Machine TranslationSegmentationTranslationLLM-to-Speech: A Synthetic Data Pipeline for Training Dialectal Text-to-Speech Models
Despite the advances in neural text to speech (TTS), many Arabic dialectal varieties remain marginally addressed, with most resources concentrated on Modern Spoken Arabic (MSA) and Gulf dialects, leaving Egyptian Arabic …
Synthetic Data GenerationSpeaker DiarizationSpeech SynthesisText to Speech