AraT5: Text-to-Text Transformers for Arabic Language Generation (2109.12068v4)

Abstract: Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects--Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with ~49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.

AraT5: Text-to-Text Transformers for Arabic Language Generation

The exploration of AraT5 models presents a comprehensive analysis dedicated to Arabic language tasks, building on text-to-text transformer methodologies. This paper primarily focuses on addressing the limitations of multilingual models like mT5 on languages with diverse dialects, such as Arabic, by illustrating the development and implementation of dedicated Arabic models.

The paper introduces three Arabic-specific T5-style models: AraT5MSA (pre-trained on Modern Standard Arabic), AraT5Tw (pre-trained on Arabic Twitter data), and AraT5 (pre-trained on a combination of MSA and Twitter data). These models are developed using approximately 49% less pre-training data than the multilingual mT5 and show enhanced performance across a comprehensive Arabic Natural Language Generation benchmark (ARGEN), which includes seven tasks: machine translation, code-switched text translation, summarization, news title generation, question generation, paraphrasing, and transliteration.

Empirical results demonstrate that the newly developed models outperform mT5 on a majority of the ARGEN evaluation tasks, achieving state-of-the-art (SOTA) results on 52 out of 59 test sets. Notably, AraT5 models surpass prior results on both the ARGEN and ARLUE (Arabic language understanding) benchmarks. The gap in performance, even under significantly reduced data constraints, substantiates the effectiveness of language-specific model design over massively multilingual architectures like mT5. Additionally, these models showcase strong zero-shot capabilities with foreign languages included in their vocabulary, demonstrating the utility of including multiple languages during pre-training.

The data preparation strategy employed in this work is meticulous, ensuring a varied and rich representation of Arabic dialects. The MSA and dialect distribution within the data reveal that a substantial portion of dialectal content is integrated, which is potentially favorable for tasks highly dependent on nuanced dialectal understanding. Code-switching is naturally occurring in the Twitter data, providing the models an edge in handling mixed-language contexts more advantageously compared to mT5, which lacked such exposure during training.

The implications of this research are multifaceted. Practically, the release of AraT5 models opens new possibilities for enhancing Arabic NLP applications, supporting a variety of tasks as outlined in ARGEN. From a theoretical perspective, it underscores the significance of constructing models that account for linguistic diversity within language families rather than broadly employing multilingual approaches, which may compromise on language-specific nuances.

Future research in AI, particularly in NLP, can expand on these findings by exploring model efficiency, model sizes, and adaptive learning techniques that could democratize access to powerful LLMs in resource-constrained environments. Additionally, enhancing the models' energy efficiency remains a critical concern for sustainability in AI research.

In conclusion, the AraT5 models contribute significantly to the field of Arabic NLP, addressing specific challenges posed by linguistic diversity and setting a precedent for similar endeavors in other underrepresented languages. The availability of these models and associated datasets augments the ecosystem of Arabic NLP resources, fostering future research and application development.