Teacher Student Multilingual

Teacher-Student Architecture for Multilingual Embeddings

This document outlines a teacher-student framework for creating aligned multilingual sentence embeddings, utilizing parallel sentence pairs.

Overview

The core idea is to train a student model to mimic the embedding behavior of a teacher model, using translated sentence pairs as a bridge across languages. This allows us to leverage the strengths of a well-established monolingual model (teacher) to guide a multilingual model (student) in producing semantically consistent embeddings across different languages.

We consider pairs of sentences, denoted as $(s, t)$, where:

• s: The original source sentence (e.g., English).

• t: The corresponding translated target sentence (e.g., French, German, or any other language).

This framework can effectively utilize multiple such pairs across various source-target language combinations.

How the Teacher-Student Architecture Works

The process involves defining teacher and student models, generating embeddings, and computing a loss function to guide the student's learning.

Model Definitions

• Teacher Model (T): Typically a strong, often monolingual, sentence embedding model (e.g., Sentence-BERT trained on a specific language).

• Student Model (S): A multilingual model capable of processing sentences from multiple languages (e.g., XLM-R).

Embedding Generation

1. Teacher's Source Embedding: The source sentence s is fed into the teacher model T to obtain its representation, denoted as $T(s)$.

2. Student's Embeddings: Both the source sentence s and the target sentence t are fed into the student model S to obtain their respective embeddings, denoted as $S(s)$ and $S(t)$.

Loss Computation (Mean Squared Error – MSE)

The training objective relies on minimizing the discrepancy between the teacher's and the student's embeddings. We compute two MSE losses:

1. Source Alignment Loss: This measures the difference between the teacher's embedding of the source sentence and the student's embedding of the same source sentence. $$ \text{MSE}_1 = \text{MSE}(T(s), S(s)) $$ This encourages the student to produce similar embeddings for the source sentence as the teacher.

2. Cross-lingual Alignment Loss: This measures the difference between the teacher's embedding of the source sentence and the student's embedding of the translated target sentence. $$ \text{MSE}_2 = \text{MSE}(T(s), S(t)) $$ This is the crucial step that bridges the language gap, encouraging the student to embed semantically equivalent sentences (the original source and its translation) into nearby points in the shared embedding space.

Training Objective

The student model is trained to minimize the combined MSE losses over a batch of $N$ parallel sentence pairs. The overall training objective is:

$$ \min \frac{1}{N} \sum_{i=1}^N \big[\text{MSE}(T(s_i), S(s_i)) + \text{MSE}(T(s_i), S(t_i))\big] $$

By minimizing these losses, the student model learns to generate embeddings that are not only consistent with the teacher's representations for the source language but also align the embeddings of translated sentences in the target language with their source counterparts.

Benefits and Flexibility

This teacher-student setup offers several advantages:

• Multilingual Alignment: It ensures that the student model generates multilingual embeddings that are aligned with the teacher's often higher-quality monolingual embeddings.

• Flexibility: The framework is highly flexible, supporting any pre-trained models as either the teacher or the student. This allows for experimentation with different architectures and language coverage.

• Shared Embedding Space: It aligns semantically equivalent sentences across languages into a shared embedding space, which is highly beneficial for various cross-lingual Natural Language Processing (NLP) tasks.

• Knowledge Distillation: It effectively distills the knowledge from a powerful monolingual teacher model into a multilingual student model.

SEO Keywords

• teacher-student framework NLP

• parallel sentence pairs alignment

• multilingual sentence embeddings training

• MSE loss in embedding alignment

• Sentence-BERT teacher model

• XLM-R student model

• cross-lingual embedding alignment

• multilingual NLP knowledge distillation

Interview Questions

• What is the primary role of parallel sentence pairs in the teacher-student framework for multilingual embeddings?

• How are the teacher and student models typically defined in this architecture for cross-lingual alignment?

• Explain the process of embedding generation for both the teacher and student models.

• Why is Mean Squared Error (MSE) loss a suitable choice for measuring the discrepancy between teacher and student embeddings?

• Describe the combined training objective in this teacher-student framework and its purpose.

• How does minimizing the MSE losses over parallel sentence pairs enable the student model to learn effective multilingual representations?

• Can this setup accommodate any pre-trained sentence embedding models as teacher or student components?

• What are the key advantages of employing a teacher-student architecture for generating multilingual sentence embeddings?

• How does this method ensure that semantically equivalent sentences across different languages are represented similarly in the embedding space?

• What types of cross-lingual NLP tasks can significantly benefit from the aligned embedding space produced by this method?