| Challenge: | Scielo database contains articles from several research domains. |
| Approach: | They propose to build a parallel corpus from Scielo in three languages: English, Portuguese, and Spanish. |
| Outcome: | The proposed system outperforms other systems on scientific articles in English, Portuguese, and Spanish. |
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SciPar: A Collection of Parallel Corpora from Scientific Abstracts (2022.lrec-1)
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Dimitrios Roussis, Vassilis Papavassiliou, Prokopis Prokopidis, Stelios Piperidis, Vassilis Katsouros
| Challenge: | SciPar is a collection of parallel corpora created from openly available metadata of bachelor theses, master theses and doctoral dissertations hosted in institutional repositories, digital libraries and national archives. |
| Approach: | They propose to harvest and process openly available metadata from repositories to extract bilingual titles and abstracts from scientific publications. |
| Outcome: | The proposed corpora could be useful for cross-lingual plagiarism detection or adapting Machine Translation systems for translation of scientific texts and academic writing in general. |
Parallel Corpora for the Biomedical Domain (L18-1)
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| Challenge: | Existing corpora of parallel corporata are being used in the biomedical domain . MT is known to support readers' access to textual documents in a language other than their native language . |
| Approach: | They propose to leverage parallel corpora to implement cross-lingual information retrieval or machine translation tools. |
| Outcome: | The proposed corpus is being used in the biomedical task at the conference on machine translation (WMT'16 and WMT'17) it can be leveraged to provide access to health information in languages other than English. |
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts (2020.lrec-1)
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| Challenge: | Existing parallel corpora for patents and scientific texts are not available due to the need for correct alignment and human curation. |
| Approach: | They develop a parallel corpus from the open access Google Patents dataset . they use Hunalign algorithm to align sentences and tokens using the largest 22 languages . |
| Outcome: | The proposed corpus is available in TSV format and with a SQLite database, with complementary information regarding patent metadata. |
Building the Spanish-Croatian Parallel Corpus (2020.lrec-1)
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| Challenge: | The first unidirectional parallel corpus spanish-croatia was built at the Faculty of Humanities and Social Sciences of the University of Zagreb. |
| Approach: | They describe the building of the first Spanish-Croatian unidirectional parallel corpus at the Faculty of Humanities and Social Sciences of the University of Zagreb. |
| Outcome: | The proposed corpus is a bilingual unidirectional (SpanishCroatian) parallel corpus . it contains 11 Spanish novels and their translations to Croatian done by six translators . |
A Multilingual Parallel Corpora Collection Effort for Indian Languages (2020.lrec-1)
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| Challenge: | Currently, neural network based approaches for machine translation are data hungry and sentence-level aligned parallel pairs are the currency. |
| Approach: | They propose to build sentence aligned parallel corpora across 10 Indian languages using online sources which have content shared across languages. |
| Outcome: | The proposed corpora significantly extends existing resources that are either not large enough or are restricted to a specific domain (such as health). |
Jojajovai: A Parallel Guarani-Spanish Corpus for MT Benchmarking (2022.lrec-1)
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Luis Chiruzzo, Santiago Góngora, Aldo Alvarez, Gustavo Giménez-Lugo, Marvin Agüero-Torales, Yliana Rodríguez
| Challenge: | a corpus of Guarani-Spanish text is presented that is aligned at sentence level . the long history of language contact between Guaran and Spanish in South America has resulted in many interesting language varieties . |
| Approach: | They propose to align Guarani-Spanish text at sentence level with 30,000 sentence pairs and a test set. |
| Outcome: | The proposed corpus contains about 30,000 sentence pairs and is structured as a collection of subsets from different sources, further split into training, development and test sets. |
Esposito: An English-Persian Scientific Parallel Corpus for Machine Translation (2024.lrec-main)
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| Challenge: | Existing scientific corpus for English-Persian language pairs is lacking . supervised neural machine translation requires millions of parallel sentences . |
| Approach: | They propose a parallel corpus called Esposito which contains 3.5 million parallel sentences . they also propose 'test sets' that might serve as a baseline for future studies . |
| Outcome: | The proposed system improves the baseline on average by 7.6 and 8.4 BLEU scores for English-Persian language pairs. |
The LTRC Hindi-Telugu Parallel Corpus (2022.lrec-1)
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| Challenge: | a qualitative corpus of 700K parallel sentences was created using multiple methods such as extract, align and review of Hindi-Telugu corpora. |
| Approach: | They propose to create a Hindi-Telugu parallel corpus of different technical domains using different methods including extract, align and review. |
| Outcome: | The proposed corpus is the largest, publicly available domain parallel corpus for Hindi-Telugu. |
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages (2022.tacl-1)
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Gowtham Ramesh, Sumanth Doddapaneni, Aravinth Bheemaraj, Mayank Jobanputra, Raghavan AK, Ajitesh Sharma, Sujit Sahoo, Harshita Diddee, Mahalakshmi J, Divyanshu Kakwani, Navneet Kumar, Aswin Pradeep, Srihari Nagaraj, Kumar Deepak, Vivek Raghavan, Anoop Kunchukuttan, Pratyush Kumar, Mitesh Shantadevi Khapra
| Challenge: | We present Samanantar, the largest publicly available parallel corpora collection for Indic languages . based on existing corporative, there has been limited benefit for resource-poor languages despite the lack of parallel corporals and monolingual corporata. |
| Approach: | They compile 12.4 million sentence pairs from existing corpora and mine 37.4 million from the Web. |
| Outcome: | The proposed model outperforms existing models and benchmarks on public datasets. |
Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts (L18-1)
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| Challenge: | Chinese and Portuguese are very populous languages, but there is not much parallel corpora in the Chinese-Portuguese language pair. |
| Approach: | They propose to curate Chinese-Portuguese parallel corpora and evaluate their quality . they extract bilingual data from government websites and use Phrased-Based Machine Translation (PBMT) and Neural Machine Translation models to build large corpus. |
| Outcome: | The proposed method can be used as a benchmark for future Chinese-Portuguese MT systems. |