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 .

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CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web (2021.acl-long)

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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)

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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.
Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction (C18-1)

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Challenge: Existing models for parallel data harvesting from Wikipedia are language-independent, robust and highly scalable.
Approach: They propose an end-to-end neural model for large-scale parallel data harvesting from Wikipedia . their model is language-independent, robust, and highly scalable .
Outcome: The proposed model is language-independent, robust, and highly scalable.
A Multilingual Dataset for Evaluating Parallel Sentence Extraction from Comparable Corpora (L18-1)

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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 .
Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation (P19-1)

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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.
Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation (C18-1)

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Challenge: Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications.
Approach: They propose a bidirectional recurrent neural network based approach to extract parallel sentences from multilingual corpora.
Outcome: The proposed approach outperforms existing approaches on noisy parallel corpora and shows significant improvements in translation performance.
SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations (2023.acl-long)

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Challenge: SpeechMatrix is a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings.
Approach: They present a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings.
Outcome: The proposed model can train bilingual models on 136 language pairs with 418 thousand hours of speech.
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse (D18-1)

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Challenge: a corpus of 43 million atomic edits is available for Wikipedia edit history . edits are instances in which a human editor has inserted a single contiguous phrase into, or deleted a contigous phrase from, an existing sentence.
Approach: They use Wikipedia edit history to mine atomic edits across 8 languages . they find edits contain instances in which a human editor has inserted a single phrase into, or deleted a contiguous phrase from, an existing sentence.
Outcome: The data show that edits differ from the language observed in standard corpora and that models trained on edits encode different aspects of semantics and discourse than models trained in raw text.
Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings (2020.tacl-1)

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Challenge: Existing methods to extract parallel sentences from unaligned text yield surprisingly good results.
Approach: They propose an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text using multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training.
Outcome: The proposed method outperforms existing methods and outperformed previous state-of-the-art methods by boosting translation performance by up to 3.5 BLEU on the WMT’14 French-English and WMT'16 German-English tasks.
CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval (2020.emnlp-main)

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Challenge: Cross-Lingual Information Retrieval (CLIR) is a retrieval task in which search queries and candidate documents are written in different languages.
Approach: They present a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia.
Outcome: The proposed datasets are the largest and most comprehensive CLIR dataset to date.

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