What are the limitations of NLP for multilingual applications?

1 Data scarcity

One of the main limitations of NLP for multilingual applications is the lack of data for many languages. Data is essential for training and evaluating NLP models, especially those that use deep learning techniques. However, most of the available data is in English or other high-resourced languages, while many languages have little or no data at all. This creates a data imbalance that affects the quality and coverage of NLP models. For example, a machine translation system may perform well for English-French, but poorly for English-Swahili. To overcome this limitation, NLP researchers and developers need to collect and annotate more data for low-resourced languages, or use techniques such as data augmentation, transfer learning, or multilingual learning.

3 Evaluation metrics

A third limitation of NLP for multilingual applications is the difficulty of evaluating the performance and usability of NLP models. Evaluation metrics are important for measuring the quality and effectiveness of NLP models, as well as for comparing and improving them. However, many evaluation metrics are language-dependent, or based on assumptions that may not hold for all languages. For example, a machine translation system may be evaluated by comparing its output with a human reference translation, but this may not account for the variability and ambiguity of natural language, or the differences in translation styles and preferences across languages. To overcome this limitation, NLP researchers and developers need to design and use evaluation metrics that are language-independent, or based on criteria that reflect the needs and expectations of the users and the tasks.

4 Ethical and social issues

A final limitation of NLP for multilingual applications is the ethical and social issues that may arise from the use and misuse of NLP models. Ethical and social issues are related to the impact and implications of NLP models on human values, rights, and responsibilities. For example, a chatbot may be used to manipulate or deceive users, a machine translation system may be used to spread misinformation or propaganda, a sentiment analysis system may be used to discriminate or stereotype users, or a text summarization system may be used to distort or omit information. To overcome this limitation, NLP researchers and developers need to consider and address the ethical and social issues of their models, or use techniques such as fairness, accountability, transparency, and explainability.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?