CreoleVal: Multilingual Multitask Benchmarks for Creoles (2310.19567v3)

Abstract: Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research.While the genealogical ties between Creoles and a number of highly-resourced languages imply a significant potential for transfer learning, this potential is hampered due to this lack of annotated data. In this work we present CreoleVal, a collection of benchmark datasets spanning 8 different NLP tasks, covering up to 28 Creole languages; it is an aggregate of novel development datasets for reading comprehension, relation classification, and machine translation for Creoles, in addition to a practical gateway to a handful of preexisting benchmarks. For each benchmark, we conduct baseline experiments in a zero-shot setting in order to further ascertain the capabilities and limitations of transfer learning for Creoles. Ultimately, we see CreoleVal as an opportunity to empower research on Creoles in NLP and computational linguistics, and in general, a step towards more equitable language technology around the globe.

• The paper presents a suite of benchmarks across eight NLP tasks for 28 Creole languages, addressing the scarcity of Creole resources.
• The paper details baseline experiments in a zero-shot transfer setting, revealing current multilingual models’ limitations for Creole data.
• The benchmarks underscore sociohistorical challenges and advocate for community-driven approaches to develop more inclusive language technologies.

CreoleVal: Multilingual Multitask Benchmarks for Creoles

The paper presents "CreoleVal," a significant contribution to the NLP community by introducing a suite of multilingual, multitask benchmarks catering specifically to Creole languages. Creoles, often marginalized and underrepresented in natural language processing research, present unique challenges and opportunities due to their linguistic and cultural complexities.

Key Contributions

1. Dataset Collection: CreoleVal encompasses datasets across eight different NLP tasks, covering up to 28 Creole languages. This comprehensive set includes new development datasets for reading comprehension, relation classification, and machine translation. Additionally, the benchmark serves as an entry point to existing datasets, consolidating resources for easier access and experimentation.
2. Baseline Experiments: The paper conducts baseline experiments in a zero-shot transfer learning setting, acknowledging the challenges posed by limited annotated data. These experiments help elucidate the capabilities and constraints of existing models when applied to Creoles.
3. Social and Historical Context: The paper highlights the sociohistorical factors affecting Creole representation in NLP. It discusses how colonialism and historical stigmatization have contributed to the lack of Creole resources and how these factors complicate data collection efforts.
4. Practical Implications: By enabling NLP research on Creoles, CreoleVal holds potential for developing language technologies that enhance technological inclusivity for Creole speakers. The benchmarks could guide future resources tailored to community-specific technological needs.

Results and Analysis

The paper quantifies the value of CreoleVal through baseline results across its datasets. For reading comprehension tasks, the paper translates the MCTest dataset into Haitian and Mauritian Creole. In the context of relation classification, Wikipedia data was used to create evaluation datasets for languages like Bislama and Chavacano. These resources allow researchers to experiment with transfer learning techniques using larger pre-trained LLMs despite the absence of Creoles during model pre-training.

The benchmark experiments reveal challenges such as low transfer performance due to the lack of Creole pre-training data, underscoring the need for additional research into effective cross-lingual and culturally sensitive transfer learning methods. The inclusion of multilingual LLMs like mBERT and XLM-R, which have limited Creole-specific data, emphasizes the necessity of more inclusive pre-training corpora.

Broader Implications for NLP and Transfer Learning

The introduction of benchmarks specific to Creoles has practical implications for the development and evaluation of NLP systems designed to work with low-resource languages. The genealogical relationship of Creoles with languages like English and French presents opportunities for targeted transfer learning strategies. These strategies could account for both lexical overlap and grammatical divergence, challenging conventional assumptions in transfer learning.

The CreoleVal benchmarks advocate for a more inclusive approach in multilingual evaluations and emphasize the importance of community involvement, crucial in ensuring that language technologies align with the needs of Creole-speaking populations.

Future Directions

The future of NLP for Creoles involves not only expanding the scope of CreoleVal with additional datasets but also exploring other modalities beyond text, such as speech. This is crucial for Creoles that are predominantly spoken. Transfer learning methodologies could be refined to better capture linguistic nuances unique to Creoles, enhancing model generalization to other truly low-resource languages.

Conclusion

CreoleVal serves as an important milestone in the intersection of language technology and linguistic diversity. It calls on researchers to consider Creole languages as integral to the landscape of NLP research, shaping the future of inclusive and culturally aware technologies. The benchmarks and datasets in CreoleVal lay the foundational work for exploring more equitable, linguistically informed NLP solutions.