Multilingual Embeddings Distillation

Learning Multilingual Embeddings Through Knowledge Distillation

This document explores a method to transform monolingual sentence embeddings into aligned multilingual embeddings using knowledge distillation. While existing multilingual models like M-BERT, XLM, and XLM-R can generate sentence representations across various languages, their embedding spaces are often not aligned. This means identical sentences expressed in different languages map to distinct points within the vector space. Our objective is to align these embeddings so that semantically similar sentences, regardless of their language, share a common vector representation.

Leveraging Sentence-BERT and XLM-R for Multilingual Embeddings

We utilize the robust sentence embedding capabilities of Sentence-BERT, primarily developed for English, and extend them to multiple languages. This is achieved by transferring the knowledge from a Sentence-BERT model to a multilingual model such as XLM-R through knowledge distillation.

Teacher-Student Framework

• Teacher Model: A pre-trained Sentence-BERT model (monolingual, English).

• Student Model: A pre-trained XLM-R model (multilingual, supporting over 100 languages).

Step-by-Step Knowledge Distillation Process

The knowledge distillation process involves training the student model to mimic the behavior of the teacher model.

1. Input Sentences: Select a pair of sentences in two different languages that are semantically equivalent.

   • Example:

     • English (Source): "How are you?"

     • French (Target): "Comment ça va?"

2. Generate Teacher Embedding: Pass the English source sentence through the teacher model (Sentence-BERT) to obtain its corresponding embedding.

3. Generate Student Embeddings: Pass both the English (source) and French (target) sentences through the student model (XLM-R) to obtain their respective embeddings.

4. Calculate Loss: Compute the Mean Squared Error (MSE) loss between:

   • The teacher's English sentence embedding and the student's English sentence embedding.

   • The teacher's English sentence embedding and the student's French sentence embedding.

   These loss calculations are crucial for aligning the student's multilingual embeddings with the teacher's monolingual embeddings.

5. Train the Student Model: Minimize the total MSE loss during the training process. This encourages the student model (XLM-R) to generate multilingual sentence embeddings that effectively replicate the teacher's high-quality monolingual embeddings across different languages.

Why Align Embeddings Across Languages?

By minimizing the MSE loss for both source and target language sentences, the student model learns to map semantically equivalent sentences from different languages to the same location in the embedding space. This alignment is vital for improving performance in cross-lingual tasks, including:

• Multilingual Information Retrieval

• Translation Retrieval

• Cross-Language Semantic Similarity Assessment

Summary

• Monolingual Sentence-BERT embeddings serve as a high-quality knowledge source (teacher).

• The multilingual XLM-R model acts as the student, learning to replicate the teacher's embedding quality across languages.

• Knowledge distillation, driven by MSE loss between teacher and student embeddings, aligns multilingual sentence vectors.

• This approach effectively creates a shared multilingual embedding space, bridging linguistic divides for various downstream NLP applications.

SEO Keywords

multilingual sentence embeddings, knowledge distillation in NLP, Sentence-BERT multilingual alignment, XLM-R multilingual embeddings, teacher-student framework NLP, cross-lingual sentence embedding alignment, MSE loss for embedding alignment, multilingual semantic similarity

Interview Questions

• What challenges arise when using multilingual models like M-BERT or XLM-R for sentence embeddings?

• How does knowledge distillation help in aligning multilingual sentence embeddings?

• Explain the teacher-student framework in the context of multilingual embeddings.

• Why is the Mean Squared Error (MSE) loss used in this knowledge distillation process?

• How does the student model (XLM-R) learn to align embeddings from different languages?

• What benefits does an aligned multilingual embedding space provide for NLP applications?

• How are teacher and student embeddings compared during training?

• Can you describe an example of input data used in multilingual embedding alignment?

• What role does Sentence-BERT play in the knowledge distillation for multilingual embeddings?

• How can aligned multilingual embeddings improve cross-language semantic similarity tasks?