# SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings

arXiv: https://arxiv.org/abs/2004.08728 · HTML: https://mlsanity.com/paper/2004.08728/

## Key facts

- Cited by 156 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2020.

## Built on by (most cited first, 30 of 156)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization](https://mlsanity.com/paper/2109.09784/) (2021)
- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation](https://mlsanity.com/paper/2302.07856/) (2023)
- [XLM-E: Cross-lingual Language Model Pre-training via ELECTRA](https://mlsanity.com/paper/2106.16138/) (2021)
- [ProoFVer: Natural Logic Theorem Proving for Fact Verification](https://mlsanity.com/paper/2108.11357/) (2021)
- [A Primer on Pretrained Multilingual Language Models](https://mlsanity.com/paper/2107.00676/) (2021)
- [MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs](https://mlsanity.com/paper/2104.08692/) (2021)
- [Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment](https://mlsanity.com/paper/2106.06381/) (2021)
- [OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining](https://mlsanity.com/paper/2311.08849/) (2023)
- [TransLLaMa: LLM-based Simultaneous Translation System](https://mlsanity.com/paper/2402.04636/) (2024)
- [Frustratingly Easy Label Projection for Cross-lingual Transfer](https://mlsanity.com/paper/2211.15613/) (2022)
- [Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models](https://mlsanity.com/paper/2205.11758/) (2022)
- [Attention as a Guide for Simultaneous Speech Translation](https://mlsanity.com/paper/2212.07850/) (2022)
- [From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text](https://mlsanity.com/paper/2107.06483/) (2021)
- [LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging](https://mlsanity.com/paper/2209.09900/) (2022)
- [MaLA-500: Massive Language Adaptation of Large Language Models](https://mlsanity.com/paper/2401.13303/) (2024)
- [Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods](https://mlsanity.com/paper/2203.05227/) (2022)
- [Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt](https://mlsanity.com/paper/2202.11451/) (2022)
- [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://mlsanity.com/paper/2402.01512/) (2024)
- [Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots](https://mlsanity.com/paper/2103.09593/) (2021)
- [Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation](https://mlsanity.com/paper/2105.04840/) (2021)
- [EmphAssess : a Prosodic Benchmark on Assessing Emphasis Transfer in Speech-to-Speech Models](https://mlsanity.com/paper/2312.14069/) (2023)
- [CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing](https://mlsanity.com/paper/2210.07074/) (2022)
- [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://mlsanity.com/paper/2210.12623/) (2022)
- [Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment](https://mlsanity.com/paper/2101.00148/) (2021)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data](https://mlsanity.com/paper/2109.04319/) (2021)
- [SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)](https://mlsanity.com/paper/2305.06586/) (2023)

## Abstract (arXiv)

Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data, and quality decreases as less training data is available. We propose word alignment methods that require no parallel data. The key idea is to leverage multilingual word embeddings, both static and contextualized, for word alignment. Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries. We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners, even with abundant parallel data; e.g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.