BERT and Out-of-Vocabulary Words in Finetuning: Handling Slang and Dialects

In the field of natural language processing (NLP), the utilization of pre-trained language models like BERT (Bidirectional Encoder Representations from Transformers) for various tasks such as text classification has become increasingly prevalent. One of the significant challenges in leveraging these models involves the handling of out-of-vocabulary (OOV) words, particularly slang and dialects. This article aims to explore whether BERT effectively learns OOV words during finetuning and assesses the efficiency of this approach.

Understanding Out-of-Vocabulary Words

Out-of-vocabulary words refer to any words that are not present in a model's pre-trained vocabulary. In the context of social media and other informal text sources, these words are often slang or dialect-specific expressions that do not have established representations in the pre-trained models. The presence of these OOV words can significantly impact the accuracy and performance of the models used for text classification.

Subword Approach in BERT

BERT, initially developed to address the limitations of the word-level vocabulary representation, employs a subword approach to deal with OOV words. The subword units used in BERT are based on the concatenation of smaller subwords, which allows for the representation of novel words by combining known subwords. This approach is inspired by the seminal paper "Neural Machine Translation of Rare Words with Subword Units" (Sennrich, Haddow, Birch, 2015), where the authors demonstrated the effectiveness of subword units in neural machine translation, particularly in handling rare and out-of-vocabulary words.

Handling OOV Words in BERT

Given that BERT relies on a subword tokenizer, it has a built-in mechanism to handle OOV words. When encountering a new word during inference or training, BERT breaks it down into smaller subword components. These subword units are then mapped to their corresponding representations in the pre-trained model, allowing the model to make predictions based on the combined representations of these subwords.

Efficiency of Learning OOV Words

The effectiveness of BERT in learning OOV words during fine-tuning is a topic of ongoing research and debate. While BERT's subword approach can handle many unknown words, it is not without limitations. The inherent challenge lies in the fact that the meaning of a word is often more than the sum of its parts, and the context in which the word is used often provides additional meaning that might not be fully captured by breaking it down into subwords.

Challenges and Limitations

One of the primary challenges in using BERT for OOV words is the potential loss of semantic information due to the breakdown of words into subwords. For instance, a complex slang expression might lose its specific meaning when reduced to a concatenation of simpler subwords. Additionally, the effectiveness of BERT in learning OOV words can be influenced by the size and quality of the training data, the model architecture, and the specific tasks at hand.

Case Study: Fine-Tuning BERT Multilingual for Text Classification

I am currently working on a project that involves fine-tuning BERT multilingual for text classification. The training dataset contains a significant number of out-of-vocabulary words, particularly slang and dialects, which are common in social media text. These words are often consistent in context and usage, but they rarely appear in the Wikipedia text corpus, which was the primary source for the multilingual BERT model's pretraining.

Observations and Results

Based on my observations, BERT does exhibit a certain degree of adaptability when encountering OOV words. During the fine-tuning process, BERT appears to learn to recognize and incorporate these words into its model, often through the subword units mechanism. However, the efficiency and accuracy of this learning process can vary significantly depending on the specific OOV words and their contextual usage.

Conclusion

While BERT's subword approach provides a robust mechanism for handling OOV words, it is not a panacea. The success of BERT in learning and utilizing OOV words in a fine-tuned context depends on various factors, including the nature of the words, the quality of the training data, and the overall context in which they appear. Future research should focus on improving the subword-based approach and exploring alternative methods to enhance BERT's performance with respect to OOV words.

References

Sennrich, R., Haddow, B., Birch, A. (2015). Neural machine translation of rare words with subword units. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1715-1725).

Devlin, J., Chang, M. W., Lee, K., Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference on Empireal Methods in Natural Language Processing (EMNLP) (pp. 4171-4186).