| Challenge: | targeted, site-specific crawling results in cleaner bitexts with a higher ratio of parallel sentences . general crawlers combined with boilerplate removal tools tend to retrieve shorter texts . |
| Approach: | They compare manual and automatic approaches to extracting bitexts from the Web . they use targeted site-specific crawling to extract cleaner bitext sentences . |
| Outcome: | The proposed methods extract more parallel sentences from the Web than manual methods. |
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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. |
Recovering document annotations for sentence-level bitext (2024.findings-acl)
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| Challenge: | In machine translation, historical models were incapable of handling longer contexts, so the lack of document-level datasets was less noticeable. |
| Approach: | They propose a document-level filtering technique that discards document- level metadata. |
| Outcome: | The proposed method improves translation without degradation of sentence-level translation. |
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia (2021.eacl-main)
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| 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 . |
Better Together: Modern Methods Plus Traditional Thinking in NP Alignment (2020.lrec-1)
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| Challenge: | a recent study shows that end-to-end systems are not structurally free. |
| Approach: | They propose to use dictionary- and word vector-based baselines to align NPs in the bitext . they argue that alignment of NP's in MT can be improved by using old-fashioned methods . |
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Trafilatura: A Web Scraping Library and Command-Line Tool for Text Discovery and Extraction (2021.acl-demo)
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| Challenge: | Existing tools for text extraction and web corpus construction are not enough to extract and pre-process web data to meet scientific expectations with respect to text quality. |
| Approach: | They propose a text discovery and extraction tool published under open-source license that allows for main text, comments and metadata extraction while also providing building blocks for web crawling tasks. |
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A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents (L18-1)
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| Challenge: | Terms are notoriously difficult to identify, both automatically and manually. |
| Approach: | They propose a method to annotate terms manually from a comparable corpus . they show that the gold standard provides a tool for evaluation and a rich source of information . |
| Outcome: | The proposed method provides a tool for evaluation and rich source of information about terms. |
Simple and Effective Paraphrastic Similarity from Parallel Translations (P19-1)
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| Challenge: | Existing methods for learning paraphrastic sentence embeddings on bitext are expensive and require manual annotation. |
| Approach: | They propose a method that trains paraphrastic sentence embeddings directly from bitext, eliminating the time-consuming step of creating paraphrase corpora. |
| Outcome: | The proposed model outperforms and is faster than state-of-the-art models on cross-lingual tasks. |
BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation (2022.findings-naacl)
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| Challenge: | Existing methods to improve Neural Machine Translation (NMT) for lowresource languages are often trained on heuristically aligned or automatically mined data. |
| Approach: | They propose to filter out imperfect translations that yield unreliable training signals for Neural Machine Translation (NMT) instead, they propose to refine mined bitexts by automatic editing . |
| Outcome: | The proposed method improves the quality of mined bitexts for low-resource languages by up to 8 BLEU points. |
BiSECT: Learning to Split and Rephrase Sentences with Bitexts (2021.emnlp-main)
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| Challenge: | Several past efforts have created Split and Rephrase training sets, which consist of long, complex input sentences paired with multiple shorter sentences that preserve the meaning of the input sentence. |
| Approach: | They propose a new dataset and a model for this task by extracting 1-2 sentence alignments from bilingual parallel corpora and using machine translation to convert both sides of the corpus into the same language. |
| Outcome: | The proposed model can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations. |
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus (2021.emnlp-main)
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Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, Matt Gardner
| Challenge: | Large text corpora are often introduced with minimal documentation . documenting collection process, composition, intended uses, and other are key for structured, task-specific datasets. |
| Approach: | They propose to document a dataset created by applying filters to a single snapshot of Common Crawl. |
| Outcome: | The proposed dataset shows that blocklist filtering removes text from minority individuals and patents. |