Improving Parallel Sentence Mining for Low-Resource and Endangered Languages (2025.acl-short)
Copied to clipboard
| Challenge: | Parallel sentence mining is a technique used to find matching sentence pairs from a source and target language. |
| Approach: | They propose a benchmark dataset for parallel sentence mining on three low-resource languages . they apply alignment post-processing and cluster-based isotropy enhancement techniques to one of them . |
| Outcome: | The proposed datasets show better mining quality overall for low-resource languages . the proposed methods are crucial for optimizing parallel data extraction for low resource languages - a new study shows. |
Similar Papers
Parallel sentences mining with transfer learning in an unsupervised setting (2021.naacl-srw)
Copied to clipboard
| Challenge: | Existing methods to mine parallel sentences in low-resource environments are not suitable for many low-level language pairs. |
| Approach: | They propose an approach based on transfer learning to mine parallel sentences in an unsupervised setting using bilingual corpora of low-resource language pairs. |
| Outcome: | The proposed model improves the performance of mined parallel sentences at two real-world low-resource language pairs compared with previous methods. |
Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Recent advances in unsupervised bilingual word embeddings make it possible to mine parallel sentences from comparable corpora. |
| Approach: | They propose a strong unsupervised system for parallel sentence mining based on cosine similarities of source and target words . they show that parallel sentences mined from real-life sources improve unsupervised MT . |
| Outcome: | The proposed system improves unsupervised MT on three language pairs. |
Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment (2024.findings-naacl)
Copied to clipboard
| Challenge: | Current approaches to obtain cross-lingual sentence embeddings rely on pre-trained language models that implicitly align the contextual representations of similar units of sentences in different languages. |
| Approach: | They propose a framework that explicitly aligns words between English and eight low-resource languages by using off-the-shelf word alignment models. |
| Outcome: | The proposed framework improves on the bitext retrieval task and in high-resource languages. |
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web (2021.acl-long)
Copied to clipboard
| Challenge: | Using a curated common crawl corpus, we were able to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English. |
| Approach: | They use 32 snapshots of a curated common crawl corpus totaling 71 billion unique sentences to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English. |
| Outcome: | The proposed system outperforms the best single systems on the WMT’19 test set for English-German/Russian/Chinese and outperformed the best submission at the 2020 WAT workshop. |
Filtering and Mining Parallel Data in a Joint Multilingual Space (P18-2)
Copied to clipboard
| Challenge: | Using a cosine distance in a joint multilingual sentence embedding, we filter out noisy parallel data and mine for bitexts in large news collections. |
| Approach: | They propose to learn a joint multilingual sentence embedding and use the distance between sentences in different languages to filter noisy parallel data and to mine for parallel data in large monolingual texts. |
| Outcome: | The proposed approach improves a competitive baseline on the WMT'14 task by 0.3 BLEU by filtering out 25% of the training data. |
Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages (2022.findings-emnlp)
Copied to clipboard
| Challenge: | a new study aims to extend multilingual representation learning beyond the hundred most frequent languages . current work on multilingual sentence representations has focused on training one model which handles all languages of interest . |
| Approach: | They propose a teacher-student approach to extend existing monolingual sentence embedding space to new languages. |
| Outcome: | The proposed model outperforms the original LASER encoder in 44 African languages . the model can be used to train multiple languages and learn new languages if they have the same training data . |
Towards the First NLP Benchmark for Ladin - an Extremely Low-Resource Language (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are limited in low-resource languages due to lack of labeled training data. |
| Approach: | They propose to use Ladin as a model for sentiment analysis and question answering by incorporating Italian data into machine translation training. |
| Outcome: | The proposed method improves on existing Italian–Ladin translation baselines. |
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia (2021.eacl-main)
Copied to clipboard
| Challenge: | a new approach to extract parallel sentences from Wikipedia articles is proposed . the approach is based on multilingual sentence embeddings, but does not limit it to English . |
| Approach: | They propose to automatically extract parallel sentences from Wikipedia articles in 96 languages . they train neural MT baseline systems on the mined data and evaluate them on the TED corpus . |
| Outcome: | The proposed approach extracts parallel sentences from Wikipedia articles in 96 languages . the extracted sentences achieve strong BLEU scores for many language pairs . |
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval (2022.findings-acl)
Copied to clipboard
| Challenge: | a new model for parallel sentence retrieval can be used to align parallel sentences in multilingual corpora . a faithful aligner can help narrow down the candidate pool without having to deal with an enormous search space . |
| Approach: | They propose a model that can be trained on only one language pair and transfers to low-resource languages with negligible degradation in performance. |
| Outcome: | The proposed model outperforms the previous model on the Tateoba dataset by 8.0 points in accuracy and using less than 0.6% of their parallel data. |
A Multilingual Dataset for Evaluating Parallel Sentence Extraction from Comparable Corpora (L18-1)
Copied to clipboard
| Challenge: | BUCC Shared Task aims to extract parallel sentences from comparable corporad . resulting corpus contains about 3.5 million distinct sentences in english, french, german, Russian, and Chinese . |
| Approach: | They present challenges faced to build a parallel sentences dataset from comparable corporad . they emphasize issues faced to include Chinese as one of the languages . |
| Outcome: | The 2017 BUCC Shared Task was a first for this task . the dataset contains 3.5 million sentences in English, French, German, Russian, and Chinese . |