Cross Lingual Pretraining

XLM Pre-Training Strategies for Cross-Lingual Models

The Cross-Lingual Language Model (XLM) leverages three key pre-training strategies to effectively learn multilingual and cross-lingual representations. These strategies are:

• Causal Language Modeling (CLM)

• Masked Language Modeling (MLM)

• Translation Language Modeling (TLM)

Each of these pre-training tasks plays a vital role in enhancing XLM’s ability to understand and generate text across multiple languages, fostering robust cross-lingual transfer learning.

Overview of XLM Pre-Training Tasks

Causal Language Modeling (CLM)

Causal Language Modeling, also known as standard language modeling or autoregressive language modeling, focuses on predicting the next word in a sequence based on the preceding words. It operates by processing text in a left-to-right direction.

Objective: To learn the probability distribution of word sequences.

How it works: Given a sequence of tokens $x_1, x_2, \dots, x_n$, the model is trained to maximize the likelihood of the sequence by predicting each token $x_i$ conditioned on its predecessors $x_1, \dots, x_{i-1}$.

$$ P(x_1, \dots, x_n) = \prod_{i=1}^n P(x_i | x_1, \dots, x_{i-1}) $$

Contribution to XLM: CLM helps XLM develop a strong understanding of language generation and sequential dependencies within a single language. This foundational understanding is crucial before extending to multilingual capabilities.

Masked Language Modeling (MLM)

Masked Language Modeling is a self-supervised objective that involves randomly masking out a percentage of tokens in the input sequence and training the model to predict these masked tokens based on their surrounding context (both left and right).

Objective: To learn deep bidirectional representations of text.

How it works:

1. Randomly select a percentage of input tokens (e.g., 15%).

2. Replace the selected tokens with a special [MASK] token, a random token, or keep them unchanged.

3. The model is then trained to predict the original identity of the masked tokens using the context provided by the unmasked tokens.

Example:

• Original Sentence: The quick brown fox jumps over the lazy dog.

• Masked Sentence: The quick [MASK] fox jumps over the [MASK] dog.

• Model's Task: Predict brown and lazy.

Contribution to XLM: MLM enables XLM to learn rich contextual representations of words, understanding their meaning based on their neighbors. This is fundamental for building contextualized embeddings that are sensitive to nuances in meaning.

Translation Language Modeling (TLM)

Translation Language Modeling is a novel pre-training task specifically designed for cross-lingual understanding. It utilizes parallel bilingual text, where sentences in two different languages that are translations of each other are concatenated and then subjected to a masked language modeling objective.

Objective: To align representations across languages and facilitate cross-lingual transfer.

How it works:

1. Take a pair of parallel sentences, one in Language A and one in Language B.

2. Concatenate these sentences into a single input sequence, often separated by a special token (e.g., [SEP]). For example: [CLS] Sentence in Language A [SEP] Sentence in Language B [SEP].

3. Apply the MLM objective: randomly mask tokens in both the Language A and Language B segments.

4. The model is trained to predict the masked tokens in either language, conditioned on the context from both languages.

Example:

• Language A: The cat sat on the mat.

• Language B: Le chat était assis sur le tapis.

• Concatenated Input (with masking): The [MASK] sat on the mat. [SEP] Le chat était assis sur le [MASK]. [SEP]

• Model's Task: Predict cat (from Language A) and tapis (from Language B).

Contribution to XLM: By forcing the model to predict masked tokens in one language using context from another, TLM explicitly trains XLM to build shared representations that bridge language barriers. This is crucial for tasks like cross-lingual retrieval, translation, and zero-shot cross-lingual classification.

How Pre-Training Tasks Complement Each Other

The three pre-training strategies are not mutually exclusive but rather work synergistically to build a powerful cross-lingual model:

• CLM provides a strong foundation in understanding language structure and generation within individual languages.

• MLM builds deep contextual understanding by enabling bidirectional reasoning over text, making representations more robust and nuanced.

• TLM bridges the gap between languages by aligning their representations, allowing the model to generalize knowledge and perform tasks across language pairs it may not have explicitly seen during supervised fine-tuning.

Together, these tasks equip XLM with the ability to process and understand text in multiple languages and transfer knowledge effectively from one language to another.

Interview Questions

Here are some common interview questions related to XLM pre-training strategies:

1. What are the three main pre-training strategies used in XLM?

   • Causal Language Modeling (CLM), Masked Language Modeling (MLM), and Translation Language Modeling (TLM).

2. How does Causal Language Modeling (CLM) work in the context of XLM?

   • It predicts the next word in a sequence, focusing on left-to-right context, to learn sequential language generation capabilities.

3. What is the purpose of Masked Language Modeling (MLM) during XLM pre-training?

   • MLM masks random tokens in the input and trains the model to predict them, enabling deep, bidirectional contextual understanding of words within a language.

4. Can you explain how Translation Language Modeling (TLM) helps XLM learn cross-lingual representations?

   • TLM uses parallel bilingual text. By masking tokens in both languages and requiring the model to predict them using context from both, it forces the model to align representations across languages.

5. Why is it beneficial to use parallel bilingual text in TLM?

   • Parallel text explicitly provides the semantic equivalence between segments in different languages, enabling the model to learn mappings and shared representations crucial for cross-lingual tasks.

6. How does MLM contribute to the model’s contextual understanding of language?

   • By predicting masked tokens using surrounding words (both before and after), MLM forces the model to learn how words interact contextually to infer meaning.

7. What are the differences between CLM and MLM?

   • CLM is unidirectional (left-to-right) and predicts the next token. MLM is bidirectional and predicts masked tokens based on surrounding context. CLM is good for generation, while MLM is good for understanding.

8. How do these pre-training tasks complement each other in improving XLM’s performance?

   • CLM builds generative abilities, MLM builds deep contextual understanding within a language, and TLM explicitly bridges language gaps by aligning representations, creating a comprehensive cross-lingual model.

9. In what ways does TLM differ from traditional masked language modeling?

   • TLM specifically uses parallel bilingual text and masks tokens in both languages, training the model to use cross-lingual context for prediction, whereas traditional MLM operates on monolingual text.

10. How do these pre-training strategies impact XLM’s ability to perform cross-lingual NLP tasks?

    • They enable XLM to understand semantic relationships across languages, perform tasks like translation or cross-lingual classification with fewer or no language-specific examples (zero-shot learning), and generalize better to unseen languages.