SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP (2020.coling-main)
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| Challenge: | Existing methods for automatic sentence alignment assume monotonic alignments, but they can handle non-monotonic alignments. |
| Approach: | They propose a method to automatically extract parallel sentences from noisy parallel documents by embeddings and encoding each source and target sentence. |
| Outcome: | The proposed method improves translation accuracy by 4.1 BLEU scores on English-Japanese . it can predict spans in target document from sentences in source document . |
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A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT (2020.emnlp-main)
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| Challenge: | supervised word alignment tools such as GIZA++, MGIZA (Gao and Vogel, 2008) and FastAlign remain stagnant in terms of word alignment accuracy. |
| Approach: | They propose a supervised word alignment method based on cross-language span prediction by formalizing a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence. |
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WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction (2023.acl-long)
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| Challenge: | Existing word alignment methods rely on manual data and lack generalization ability. |
| Approach: | They propose to use a weakly-supervised large-scale weakly supervised dataset for word alignment pre-training via span prediction to reduce the need for manual data. |
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Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast (2021.emnlp-main)
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| Challenge: | Existing work uses sentences within the same batch as negatives, which suffers from easy negatives. |
| Approach: | They propose to align sentence representations from different languages into a unified embedding space . they adapt MoCo to further improve the quality of alignment . |
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Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment (2022.emnlp-main)
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| Challenge: | Existing word alignment models capture few interactions between input sentence pairs, which severely degrades the word alignment quality. |
| Approach: | They propose to model deep interactions between input and target sentences using a two-stage training framework to train the model. |
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SentAlign: Accurate and Scalable Sentence Alignment (2023.emnlp-demo)
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| Challenge: | SentAlign is an automatic sentence alignment tool designed for large documents . it evaluates all possible alignment paths in documents of thousands of sentences . |
| Approach: | They present a sentence alignment tool that evaluates all possible alignment paths in parallel documents of thousands of sentences and uses a divide-and-conquer approach to align documents containing tens of thousands. |
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SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings (2020.findings-emnlp)
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| Challenge: | Word alignments are useful for statistical and neural machine translation (NMT) and cross-lingual annotation projection. |
| Approach: | They propose to leverage multilingual word embeddings for word alignment. |
| Outcome: | The proposed methods perform better for four languages and comparable for two languages than traditional statistical aligners even with abundant parallel data. |
Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover’s Distance (2020.aacl-main)
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| Challenge: | Document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. |
| Approach: | They propose an unsupervised scoring function that leverages cross-lingual sentence embeddings to compute the semantic distance between documents in different languages. |
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Word Alignment by Fine-tuning Embeddings on Parallel Corpora (2021.eacl-main)
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| Challenge: | Existing work on word alignment has focused on unsupervised learning on parallel text. |
| Approach: | They propose to combine pre-trained contextualized word embeddings with multilingually trained language models to achieve competitive results on word alignment tasks. |
| Outcome: | The proposed model outperforms state-of-the-art models on five language pairs and can train multilingual word aligners that can obtain robust performance on different language pairs. |
Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
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Neural Network Alignment for Sentential Paraphrases (P19-1)
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| Challenge: | a monolingual alignment system is ill-suited for word- or short phrase-based alignments. |
| Approach: | They propose a monolingual alignment system for long, sentence- or clause-level alignments . they show that systems designed for word- or short phrase-based alignment are ill-suited for longer alignments. |
| Outcome: | The proposed system outperforms state-of-the-art systems on long alignments . it achieves significantly higher recall on aligning phrases of four or more words . |