Abstract
This study investigates the integration of the XLM-R cross-lingual pre-trained language model into neural machine translation (NMT) systems, focusing on three architectural variants:
- Source-Language Enhancement: XLM-R replaces the Transformer encoder for improved source sentence encoding.
- Target-Language Enhancement: XLM-R augments the Transformer decoder with an additional attention-based subnetwork.
- Dual-Language Enhancement: Simultaneous incorporation of XLM-R at both encoder and decoder levels.
Experiments on WMT English-German (high-resource) and IWSLT English-Portuguese/English-Vietnamese (low-resource) tasks demonstrate that:
- For high-resource settings, XLM-R significantly improves source encoding quality.
- For low-resource scenarios, XLM-R supplements both source and target language knowledge, overcoming data scarcity limitations.
Keywords: Cross-lingual pre-training, Neural Machine Translation, Transformer architecture, XLM-R optimization, Fine-tuning strategies
Introduction
Recent advances in pre-trained language models (e.g., BERT, GPT) have revolutionized natural language processing. However, their application to machine translation—particularly in multilingual contexts—requires specialized adaptation. This paper addresses this gap by systematically integrating XLM-R, a state-of-the-art multilingual model, into Transformer-based NMT systems.
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Methodology
1. Model Architectures
1.1 XLM-R-ENC Model
- Implementation: Replaces Transformer encoder with XLM-R’s 12-layer architecture.
- Key Benefit: Leverages XLM-R’s multilingual token embeddings for superior source sentence representation.
1.2 XLM-R-DEC Model
- Innovation: Augments decoder with a 6-layer Add_Dec subnetwork to align XLM-R’s masked language modeling with autoregressive translation.
- Challenge: Requires modification of XLM-R’s attention masking (Figure 3).
1.3 XLM-R-ENC&DEC Model
- Hybrid Approach: Combines strengths of both encoder and decoder enhancements.
2. Training Strategies
Three parameter optimization approaches were compared:
- Direct Fine-Tuning: Full model optimization (best for low-resource tasks).
- Freeze-and-Train: Fixed XLM-R parameters with task-specific layer training.
- Progressive Unfreezing: Gradual fine-tuning after initial frozen training.
Experimental Results
Dataset Overview
| Corpus | Train Pairs | Validation | Test Set |
|-----------------|------------|------------|---------|
| WMT14 En-De | 446,884 | 3,000 | 2,737 |
| IWSLT17 En-Pt | 171,032 | 1,155 | 1,124 |
| IWSLT15 En-Vi | 133,317 | 1,553 | 1,268 |
Performance Comparison (BLEU Scores)
| Model | En-De | En-Pt | En-Vi |
|---------------------|-------|-------|-------|
| Transformer Base | 27.22 | 34.86 | 26.12 |
| XLM-R-ENC | 29.07 | 39.22 | 31.39 |
| XLM-R-ENC&DEC | 24.51 | 37.95 | 30.98 |
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Key Findings
- High-Resource Tasks: XLM-R-ENC outperforms baselines by +1.85 BLEU (En-De).
- Low-Resource Tasks: XLM-R-ENC&DEC achieves near-parity with XLM-R-ENC despite decoder challenges.
- Training Efficiency: Direct fine-tuning yields optimal results (Table 3).
FAQ Section
Q1: Why does XLM-R-DEC underperform?
A1: The mismatch between XLM-R’s masked language modeling and NMT’s autoregressive generation limits decoder effectiveness.
Q2: How does XLM-R handle rare tokens?
A2: Shared multilingual vocabulary enables cross-lingual knowledge transfer for unseen tokens (Table 6).
Q3: Is layer depth critical for performance?
A3: Full 12-layer XLM-R usage maximizes gains, though 6-layer configurations remain viable (Table 4).