OmniAlign: A Unified Multilingual Aligner for Word and Sentence Alignment
OmniAlign unifies multilingual word and sentence alignment, excelling in long-text scenarios.
Key Findings
Methodology
OmniAlign uses an mGTE-based encoder and a four-stage training pipeline: continued pre-training, self-supervised learning, supervised fine-tuning, and sentence embedding distillation. It employs dynamic programming for m–n sentence alignment and contextualized embeddings for word alignment.
Key Results
- Achieved an average AER of 8.5% across nine language pairs in word alignment, ranking first on four datasets.
- Ranked first in F1 scores on four out of seven sentence alignment datasets, with a standout F1 of 0.970 for Chinese-English alignment.
- Ablation studies showed each training stage significantly improved performance, reducing AER from 43.0% to 8.5%.
Significance
OmniAlign addresses the fragmentation of word and sentence alignment tools by unifying them into a single multilingual model. It demonstrates robust performance on long texts and generalizes well to unseen language pairs, benefiting machine translation and cross-lingual information retrieval.
Technical Contribution
Introduced a lightweight, unified multilingual alignment model supporting long-text and multi-granularity alignment. Achieved efficient alignment using dynamic programming and contextualized embeddings, outperforming state-of-the-art methods.
Novelty
OmniAlign is the first model to unify multilingual word and sentence alignment, excelling in long-text alignment and unseen language pair generalization.
Limitations
- Performance on low-resource languages remains limited due to insufficient annotated data.
- Sentence alignment is slightly weaker for certain pairs like German-French compared to specialized models.
- High computational requirements may limit deployment in resource-constrained settings.
Future Work
Future work could explore enhancing low-resource language performance, optimizing long-text alignment efficiency, and expanding to more language pairs and domain-specific tasks.
AI Executive Summary
OmniAlign is a unified multilingual alignment model addressing the need for separate tools for word and sentence alignment. Built on an mGTE encoder, it uses contextualized embeddings for word alignment and dynamic programming for m–n sentence alignment.
Experiments demonstrate OmniAlign's strong performance, achieving an average AER of 8.5% across nine language pairs in word alignment and leading F1 scores in four out of seven sentence alignment datasets. Notably, it excelled in Chinese-English alignment with an F1 score of 0.970.
While OmniAlign shows exceptional performance and generalization, challenges remain in low-resource languages and computational efficiency. Future research could focus on these areas and explore applications in multilingual knowledge graph construction and domain-specific alignments.
Deep Analysis
Background
Cross-lingual sequence alignment is crucial for building parallel corpora, enabling tasks like machine translation and cross-lingual NLP. Existing tools often specialize in either word or sentence alignment, leaving a gap for a unified solution.
Core Problem
Current alignment tools struggle with long-text and multilingual scenarios, requiring multiple systems to handle different granularities, increasing complexity and maintenance costs.
Innovation
OmniAlign unifies word and sentence alignment in a single model, leveraging a four-stage training pipeline and dynamic programming for efficient alignment across languages and text lengths.
Methodology
- �� Built on the mGTE encoder, supporting long-text and multilingual alignment.
- �� Continued pre-training enhances semantic representations.
- �� Self-supervised learning uses pseudo-labels for word alignment.
- �� Supervised fine-tuning improves accuracy with human-annotated data.
- �� Knowledge distillation restores sentence embedding capabilities.
Experiments
Experiments used nine language pairs for word alignment and seven for sentence alignment, evaluating AER and F1 scores. Baselines included FastAlign, GIZA++, and BertAlign.
Results
OmniAlign achieved an average AER of 8.5% in word alignment and an F1 score of 0.970 for Chinese-English sentence alignment, outperforming most baselines.
Applications
Applicable to machine translation, cross-lingual information retrieval, and multilingual corpus construction, particularly in long-text and multilingual settings.
Limitations & Outlook
Challenges include limited performance on low-resource languages, weaker results on some language pairs, and high computational requirements.
Plain Language Accessible to non-experts
Imagine you're matching pages in books written in different languages. OmniAlign is like a smart assistant that not only matches the pages but also aligns individual words. It learns from a vast library of multilingual books to understand patterns and ensures accurate alignment even for long texts.
ELI14 Explained like you're 14
Think of OmniAlign as a puzzle master for languages! It matches sentences and words between, say, English and Chinese, like solving a giant language puzzle. It uses some super cool learning tricks, like practicing on lots of puzzles and learning from a master, to get really good at it. Awesome, right?
Glossary
OmniAlign
A unified multilingual alignment model supporting both word and sentence alignment.
Used for cross-lingual sequence alignment tasks.
mGTE
A multilingual encoder model supporting long-text inputs.
Serves as the backbone for OmniAlign.
Dynamic Programming
An optimization algorithm for solving m–n sentence alignment.
Used in OmniAlign's sentence alignment process.
AER
Alignment Error Rate, a metric for evaluating alignment accuracy.
Measures word alignment performance.
Knowledge Distillation
A training method where a teacher model guides a student model.
Used to restore sentence embedding capabilities in OmniAlign.
Open Questions Unanswered questions from this research
- 1 How can low-resource language alignment be improved?
- 2 What methods can reduce computational complexity for long-text alignment?
- 3 Can OmniAlign be extended to domain-specific alignment tasks?
Applications
Immediate Applications
Machine Translation
Enhances translation quality by providing high-precision alignments, especially for long texts.
Cross-Lingual Information Retrieval
Enables efficient retrieval and matching of multilingual documents.
Long-term Vision
Multilingual Knowledge Graph Construction
Facilitates building cross-lingual knowledge graphs, advancing multilingual AI.
Abstract
Cross-lingual sequence alignment is fundamental for building and exploiting parallel corpora, spanning mappings from documents and sentences down to words and subwords. Existing tools, however, typically specialize in a single granularity, so practitioners often need separate systems for word- and sentence-level alignment---especially in multilingual and long-text settings. We present OmniAlign, a unified multilingual aligner that supports both word-level and sentence-level alignment with a single lightweight model. Built on an encoder-only backbone with strong long-context modeling, OmniAlign induces word alignments from contextualized token similarity matrices, and obtains document-level $m$--$n$ sentence alignments via sentence embeddings combined with dynamic programming. To balance fine-grained alignment accuracy and sentence-representation quality, we use a four-stage training pipeline: alignment-oriented continued pre-training, self-supervised learning, supervised fine-tuning on human annotations, and sentence-embedding distillation from a strong multilingual teacher. Experiments show that OmniAlign achieves highly competitive performance on both word- and sentence-alignment benchmarks and generalizes well to unseen language pairs. Surprisingly, later-stage supervised fine-tuning on short texts further improves alignment quality while retaining the long-context understanding acquired in earlier training, keeping the model robust on long-text word alignment. \normalsize {\color{blue}\textbf{Code}: https://github.com/MilkDargon/OmniAlign}\par {\color{blue}\textbf{Model}: https://huggingface.co/WPS-Qingqiu/OmniAlign}