    @inproceedings{moslem-etal-2022-domain,
      title = "Domain-Specific Text Generation for Machine Translation",
      author = "Moslem, Yasmin  and
      Haque, Rejwanul  and
      Kelleher, John  and
      Way, Andy",
      booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
      month = sep,
      year = "2022",
      address = "Orlando, USA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/2022.amta-research.2",
      pages = "14--30",
      abstract = "Preservation of domain knowledge from the source to target is crucial in any translation workflow. It is common in the translation industry to receive highly-specialized projects, where there is hardly any parallel in-domain data. In such scenarios where there is insufficient in-domain data to fine-tune Machine Translation (MT) models, producing translations that are consistent with the relevant context is challenging. In this work, we propose leveraging state-of-the-art pretrained language models (LMs) for domain-specific data augmentation for MT, simulating the domain characteristics of either (a) a small bilingual dataset, or (b) the monolingual source text to be translated. Combining this idea with back-translation, we can generate huge amounts of synthetic bilingual in-domain data for both use cases. For our investigation, we used the state-of-the-art MT architecture, Transformer. We employed mixed fine-tuning to train models that significantly improve translation of in-domain texts. More specifically, our proposed methods achieved improvements of approximately 5-6 BLEU and 2-3 BLEU, respectively, on Arabic-to-English and English-to-Arabic language pairs. Furthermore, the outcome of human evaluation corroborates the automatic evaluation results.",
    }

    @inproceedings{BarHillel2003ADO,
      title={A Demonstration of the Nonfeasibility of Fully Automatic High Quality Translation},
      author={Yehoshua Bar-Hillel},
      year={1960},
    }

    @article{brown1990statistical,
      title={A statistical approach to machine translation},
      author={Brown, Peter F and Cocke, John and Della Pietra, Stephen A and Della Pietra, Vincent J and Jelinek, Frederick and Lafferty, John and Mercer, Robert L and Roossin, Paul S},
      journal={Computational linguistics},
      volume={16},
      number={2},
      pages={79--85},
      year={1990}
    }

    @inproceedings{och-2003-minimum,
        title = "Minimum Error Rate Training in Statistical Machine Translation",
        author = "Och, Franz Josef",
        booktitle = "Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics",
        month = jul,
        year = "2003",
        address = "Sapporo, Japan",
        publisher = "Association for Computational Linguistics",
        url = "https://aclanthology.org/P03-1021",
        doi = "10.3115/1075096.1075117",
        pages = "160--167",
    }
    @book{Bird_Natural_Language_Processing_2009,author = {Bird, Steven and Klein, Ewan and Loper, Edward},publisher = {O'Reilly Media, Inc.},title = {{Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit}},year = {2009}}
    
    @misc{post2018clarity,
      title={A Call for Clarity in Reporting BLEU Scores}, 
      author={Matt Post},
      year={2018},
      eprint={1804.08771},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
    }
    
    @inproceedings{jain-etal-1991-connectionist,
    title = "Connectionist and Symbolic Processing in Speech-to-Speech Translation: The {JANUS} System",
    author = "Jain, A. N.  and
      McNair, A. E.  and
      Waibel, A.  and
      Saito, H.  and
      Hauptmann, A.G.  and
      Tebelskis, J.",
    booktitle = "Proceedings of Machine Translation Summit III: Papers",
    month = jul # " 1-4",
    year = "1991",
    address = "Washington DC, USA",
    url = "https://aclanthology.org/1991.mtsummit-papers.18",
    pages = "113--117",
    abstract = "We present JANUS, a speech-to-speech translation system that utilizes diverse processing strategies including connectionist learning, traditional AI knowledge representation approaches, dynamic programming, and stochastic techniques. JANUS translates continuously spoken English utterances into Japanese and German speech utterances. The overall system performance on a corpus of conference registration conversations is 87{\%}. Two versions of JANUS are compared: one using an LR parser (JANUS-LR) and one using a neural-network based parser (JANUS-NN). Performance results are mixed, with JANUS-LR deriving benefit from a tighter language model and JANUS-NN benefiting from greater flexibility.",
    }

   @inproceedings{castano-etal-1997-machine,
        title = "Machine translation using neural networks and finite-state models",
        author = "Casta{\~n}o, Asunci{\'o}n  and
          Casacuberta, Francisco  and
          Vidal, Enrique",
        booktitle = "Proceedings of the 7th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages",
        month = jul # " 23-25",
        year = "1997",
        address = "St John{'}s College, Santa Fe",
        publisher = "?? Not mentionned on TOC",
        url = "https://aclanthology.org/1997.tmi-1.19",
    }
    
    @inproceedings{schwenk-etal-2006-continuous,
        title = "Continuous Space Language Models for Statistical Machine Translation",
        author = "Schwenk, Holger  and
          Dechelotte, Daniel  and
          Gauvain, Jean-Luc",
        booktitle = "Proceedings of the {COLING}/{ACL} 2006 Main Conference Poster Sessions",
        month = jul,
        year = "2006",
        address = "Sydney, Australia",
        publisher = "Association for Computational Linguistics",
        url = "https://aclanthology.org/P06-2093",
        pages = "723--730",
    }

    @misc{devlin2019bert,
      title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding}, 
      author={Jacob Devlin and Ming-Wei Chang and Kenton Lee and Kristina Toutanova},
      year={2019},
      eprint={1810.04805},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
    }
    
    @article{gpt,
      added-at = {2020-07-14T16:37:42.000+0200},
      author = {Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya},
      biburl = {https://www.bibsonomy.org/bibtex/273ced32c0d4588eb95b6986dc2c8147c/jonaskaiser},
      interhash = {5c343ed9a31ac52fd17a898f72af228f},
      intrahash = {73ced32c0d4588eb95b6986dc2c8147c},
      keywords = {final thema:transformer},
      timestamp = {2020-07-14T16:49:42.000+0200},
      title = {Improving language understanding by generative pre-training},
      year = 2018
    }

    @misc{sennrich2016neural,
      title={Neural Machine Translation of Rare Words with Subword Units}, 
      author={Rico Sennrich and Barry Haddow and Alexandra Birch},
      year={2016},
      eprint={1508.07909},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
    }

    @article{DBLP:journals/corr/SutskeverVL14,
      author    = {Ilya Sutskever and
                   Oriol Vinyals and
                   Quoc V. Le},
      title     = {Sequence to Sequence Learning with Neural Networks},
      journal   = {CoRR},
      volume    = {abs/1409.3215},
      year      = {2014},
      url       = {http://arxiv.org/abs/1409.3215},
      eprinttype = {arXiv},
      eprint    = {1409.3215},
      timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
      biburl    = {https://dblp.org/rec/journals/corr/SutskeverVL14.bib},
      bibsource = {dblp computer science bibliography, https://dblp.org}
    }

    
    @article{DBLP:journals/corr/ChoMGBSB14,
      author    = {Kyunghyun Cho and
                   Bart van Merrienboer and
                   {\c{C}}aglar G{\"{u}}l{\c{c}}ehre and
                   Fethi Bougares and
                   Holger Schwenk and
                   Yoshua Bengio},
      title     = {Learning Phrase Representations using {RNN} Encoder-Decoder for Statistical
                   Machine Translation},
      journal   = {CoRR},
      volume    = {abs/1406.1078},
      year      = {2014},
      url       = {http://arxiv.org/abs/1406.1078},
      eprinttype = {arXiv},
      eprint    = {1406.1078},
      timestamp = {Mon, 13 Aug 2018 16:46:44 +0200},
      biburl    = {https://dblp.org/rec/journals/corr/ChoMGBSB14.bib},
      bibsource = {dblp computer science bibliography, https://dblp.org}
    }

    @article{Firth_linguistic-theory,
        author="FIRTH J. R.",
        title="A synopsis of linguistic theory, 1930-1955",
        journal="Studies in Linguistic Analysis",
        publisher="Basil Blackwell",
        year="1957",
        URL="https://cir.nii.ac.jp/crid/1570854175539816192"
    }

    @misc{s2s_scheme_inspiration,
      title = {A Simple Introduction to Sequence to Sequence Models},
      howpublished = {\url{https://www.analyticsvidhya.com/blog/2020/08/a-simple-introduction-to-sequence-to-sequence-models/}}
    }
    
    @article{DBLP:journals/corr/ChoMBB14,
      author    = {KyungHyun Cho and
                   Bart van Merrienboer and
                   Dzmitry Bahdanau and
                   Yoshua Bengio},
      title     = {On the Properties of Neural Machine Translation: Encoder-Decoder Approaches},
      journal   = {CoRR},
      volume    = {abs/1409.1259},
      year      = {2014},
      url       = {http://arxiv.org/abs/1409.1259},
      eprinttype = {arXiv},
      eprint    = {1409.1259},
      timestamp = {Mon, 13 Aug 2018 16:47:23 +0200},
      biburl    = {https://dblp.org/rec/journals/corr/ChoMBB14.bib},
      bibsource = {dblp computer science bibliography, https://dblp.org}
    }
    
    @misc{s2s_attention,
        doi = {10.48550/ARXIV.1409.0473},
        url = {https://arxiv.org/abs/1409.0473},
        author = {Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},
        keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
        title = {Neural Machine Translation by Jointly Learning to Align and Translate},
        publisher = {arXiv},
        year = {2014},
        copyright = {arXiv.org perpetual, non-exclusive license}
    }

    @misc{vaswani2017attention,
      doi = {10.48550/ARXIV.1706.03762},
      url = {https://arxiv.org/abs/1706.03762},
      author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
      keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
      title = {Attention Is All You Need},
      publisher = {arXiv},
      year = {2017},  
      copyright = {arXiv.org perpetual, non-exclusive license}
    }

    @article{DBLP:journals/corr/SennrichHB15,
      author    = {Rico Sennrich and
                   Barry Haddow and
                   Alexandra Birch},
      title     = {Neural Machine Translation of Rare Words with Subword Units},
      journal   = {CoRR},
      volume    = {abs/1508.07909},
      year      = {2015},
      url       = {http://arxiv.org/abs/1508.07909},
      eprinttype = {arXiv},
      eprint    = {1508.07909},
      timestamp = {Mon, 13 Aug 2018 16:47:17 +0200},
      biburl    = {https://dblp.org/rec/journals/corr/SennrichHB15.bib},
      bibsource = {dblp computer science bibliography, https://dblp.org}
    }
    @inproceedings{papineni-etal-2002-bleu,
    title = "{B}leu: a Method for Automatic Evaluation of Machine Translation",
    author = "Papineni, Kishore  and
      Roukos, Salim  and
      Ward, Todd  and
      Zhu, Wei-Jing",
    booktitle = "Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2002",
    address = "Philadelphia, Pennsylvania, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P02-1040",
    doi = "10.3115/1073083.1073135",
    pages = "311--318",
}
    @InProceedings{opus,
  author = {Jörg Tiedemann},
  title = {Parallel Data, Tools and Interfaces in OPUS},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
 }
 @inproceedings{chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
    @misc{CCMatrix,
      doi = {10.48550/ARXIV.1911.04944},
      url = {https://arxiv.org/abs/1911.04944},
      author = {Schwenk, Holger and Wenzek, Guillaume and Edunov, Sergey and Grave, Edouard and Joulin, Armand},
      keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
      title = {CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB},
      publisher = {arXiv},
      year = {2019},  
      copyright = {arXiv.org perpetual, non-exclusive license}
    }
    @article{tatoeba,
    title = "Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond",
    author = "Artetxe, Mikel  and
      Schwenk, Holger",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    year = "2019",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/Q19-1038",
    doi = "10.1162/tacl_a_00288",
    pages = "597--610",
    abstract = "We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts. Our system uses a single BiLSTM encoder with a shared byte-pair encoding vocabulary for all languages, which is coupled with an auxiliary decoder and trained on publicly available parallel corpora. This enables us to learn a classifier on top of the resulting embeddings using English annotated data only, and transfer it to any of the 93 languages without any modification. Our experiments in cross-lingual natural language inference (XNLI data set), cross-lingual document classification (MLDoc data set), and parallel corpus mining (BUCC data set) show the effectiveness of our approach. We also introduce a new test set of aligned sentences in 112 languages, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low- resource languages. Our implementation, the pre-trained encoder, and the multilingual test set are available at https://github.com/facebookresearch/LASER.",
}
    @article{flores-200,
      author    = {NLLB Team and Marta R. Costa-jussà and James Cross and Onur Çelebi and Maha Elbayad and Kenneth Heafield and Kevin Heffernan and Elahe Kalbassi and  Janice Lam and Daniel Licht and Jean Maillard and Anna Sun and Skyler Wang and Guillaume Wenzek and Al Youngblood and Bapi Akula and Loic Barrault and Gabriel Mejia Gonzalez and Prangthip Hansanti and John Hoffman and Semarley Jarrett and Kaushik Ram Sadagopan and Dirk Rowe and Shannon Spruit and Chau Tran and Pierre Andrews and Necip Fazil Ayan and Shruti Bhosale and Sergey Edunov and Angela Fan and Cynthia Gao and Vedanuj Goswami and Francisco Guzmán and Philipp Koehn and Alexandre Mourachko and Christophe Ropers and Safiyyah Saleem and Holger Schwenk and Jeff Wang},
      title     = {No Language Left Behind: Scaling Human-Centered Machine Translation},
      year      = {2022}
    }

    @inproceedings{duan-etal-2020-bilingual,
    title = "Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences",
    author = "Duan, Xiangyu  and
      Ji, Baijun  and
      Jia, Hao  and
      Tan, Min  and
      Zhang, Min  and
      Chen, Boxing  and
      Luo, Weihua  and
      Zhang, Yue",
        booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
        month = jul,
        year = "2020",
        address = "Online",
        publisher = "Association for Computational Linguistics",
        url = "https://aclanthology.org/2020.acl-main.143",
        doi = "10.18653/v1/2020.acl-main.143",
        pages = "1570--1579",
        abstract = "In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary. Motivated by the ability of a monolingual speaker learning to translate via looking up the bilingual dictionary, we propose the task to see how much potential an MT system can attain using the bilingual dictionary and large scale monolingual corpora, while is independent on parallel sentences. We propose anchored training (AT) to tackle the task. AT uses the bilingual dictionary to establish anchoring points for closing the gap between source language and target language. Experiments on various language pairs show that our approaches are significantly better than various baselines, including dictionary-based word-by-word translation, dictionary-supervised cross-lingual word embedding transformation, and unsupervised MT. On distant language pairs that are hard for unsupervised MT to perform well, AT performs remarkably better, achieving performances comparable to supervised SMT trained on more than 4M parallel sentences.",
    }

    @misc{sennrich2016improving,
      title={Improving Neural Machine Translation Models with Monolingual Data}, 
      author={Rico Sennrich and Barry Haddow and Alexandra Birch},
      year={2016},
      eprint={1511.06709},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
    }
    @inproceedings{abdelali-etal-2014-amara,
    title = "The {AMARA} Corpus: Building Parallel Language Resources for the Educational Domain",
    author = "Abdelali, Ahmed  and
      Guzman, Francisco  and
      Sajjad, Hassan  and
      Vogel, Stephan",
    booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
    month = may,
    year = "2014",
    address = "Reykjavik, Iceland",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/877_Paper.pdf",
    pages = "1856--1862",
    abstract = "This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel, community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs, designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content.",
}

    @inproceedings{Azkarate2014TheWA,
      title={The web as a corpus of basque},
      author={Igor Azkarate},
      year={2014}
    }

    @inproceedings{koehn-2005-europarl,
        title = "{E}uroparl: A Parallel Corpus for Statistical Machine Translation",
        author = "Koehn, Philipp",
        booktitle = "Proceedings of Machine Translation Summit X: Papers",
        month = sep # " 13-15",
        year = "2005",
        address = "Phuket, Thailand",
        url = "https://aclanthology.org/2005.mtsummit-papers.11",
        pages = "79--86",
        abstract = "We collected a corpus of parallel text in 11 languages from the proceedings of the European Parliament, which are published on the web. This corpus has found widespread use in the NLP community. Here, we focus on its acquisition and its application as training data for statistical machine translation (SMT). We trained SMT systems for 110 language pairs, which reveal interesting clues into the challenges ahead.",
    }

    @InProceedings{TIEDEMANN12.463,
      author = {Jörg Tiedemann},
      title = {Parallel Data, Tools and Interfaces in OPUS},
      booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
      year = {2012},
      month = {may},
      date = {23-25},
      address = {Istanbul, Turkey},
      editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
      publisher = {European Language Resources Association (ELRA)},
      isbn = {978-2-9517408-7-7},
      language = {english}
     }

     @misc{reimers2020making,
      title={Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation}, 
      author={Nils Reimers and Iryna Gurevych},
      year={2020},
      eprint={2004.09813},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
    }

     @article{WOLK2014126,
        title = {Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs},
        journal = {Procedia Technology},
        volume = {18},
        pages = {126-132},
        year = {2014},
        note = {International workshop on Innovations in Information and Communication Science and Technology, IICST 2014, 3-5 September 2014, Warsaw, Poland},
        issn = {2212-0173},
        doi = {https://doi.org/10.1016/j.protcy.2014.11.024},
        url = {https://www.sciencedirect.com/science/article/pii/S2212017314005453},
        author = {Krzysztof Wołk and Krzysztof Marasek},
        keywords = {Comparable corpora, machine translation, NLP},
        abstract = {Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our methodology for mining such data from previously obtained comparable corpora. The task is highly practical since non-parallel multilingual data exist in far greater quantities than parallel corpora, but parallel sentences are a much more useful resource. Here we propose a web crawling method for building subject-aligned comparable corpora from Wikipedia articles. We also introduce a method for extracting truly parallel sentences that are filtered out from noisy or just comparable sentence pairs. We describe our implementation of a specialized tool for this task as well as training and adaption of a machine translation system that supplies our filter with additional information about the similarity of comparable sentence pairs.}
    }
    @inproceedings{reimers-2020-multilingual-sentence-bert,
        title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
        author = "Reimers, Nils and Gurevych, Iryna",
        booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
        month = "11",
        year = "2020",
        publisher = "Association for Computational Linguistics",
        url = "https://arxiv.org/abs/2004.09813",
    }
    
    @misc{lample2019crosslingual,
      title={Cross-lingual Language Model Pretraining}, 
      author={Guillaume Lample and Alexis Conneau},
      year={2019},
      eprint={1901.07291},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
    }
    @article{conneau2017word,
      title={Word Translation Without Parallel Data},
      author={Conneau, Alexis and Lample, Guillaume and Ranzato, Marc'Aurelio and Denoyer, Ludovic and J{\'e}gou, Herv{\'e}},
      journal={arXiv preprint arXiv:1710.04087},
      year={2017}
    }
    @article{10.2307/41487458,
     ISSN = {09226567, 15730573},
     URL = {http://www.jstor.org/stable/41487458},
     abstract = {Apertium is a free/open-source platform for rule-based machine translation. It is being widely used to build machine translation systems for a variety of language pairs, especially in those cases (mainly with related-language pairs) where shallow transfer suffices to produce good quality translations, although it has also proven useful in assimilation scenarios with more distant pairs involved. This article summarises the Apertium platform: the translation engine, the encoding of linguistic data, and the tools developed around the platform. The present limitations of the platform and the challenges posed for the coming years are also discussed. Finally, evaluation results for some of the most active language pairs are presented. An appendix describes Apertium as a free/open-source project.},
     author = {Mikel L. Forcada and Mireia Ginestí-Rosell and Jacob Nordfalk and Jim O'Regan and Sergio Ortiz-Rojas and Juan Antonio Pérez-Ortiz and Felipe Sánchez-Martínez and Gema Ramírez-Sánchez and Francis M. Tyers},
     journal = {Machine Translation},
     number = {2},
     pages = {127--144},
     publisher = {Springer},
     title = {Apertium: a free/open-source platform for rule-based machine translation},
     urldate = {2023-04-12},
     volume = {25},
     year = {2011}
    }

    @article{PLN4867,
	author = {O'Regan, Jim and Forcada, Mikel~L.},
	title = {Peeking through the language barrier: the development of a free/open-source gisting system for Basque to English based on apertium.org},
	journal = {Procesamiento del Lenguaje Natural},
	volume = {51},
	number = {0},
	year = {2013},
	issn = {1989-7553},	
	url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/4867}
    }


    @inproceedings{conneau-etal-2020-unsupervised,
    title = "Unsupervised Cross-lingual Representation Learning at Scale",
    author = "Conneau, Alexis  and
      Khandelwal, Kartikay  and
      Goyal, Naman  and
      Chaudhary, Vishrav  and
      Wenzek, Guillaume  and
      Guzm{\'a}n, Francisco  and
      Grave, Edouard  and
      Ott, Myle  and
      Zettlemoyer, Luke  and
      Stoyanov, Veselin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.747",
    doi = "10.18653/v1/2020.acl-main.747",
    pages = "8440--8451",
    abstract = "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6{\%} average accuracy on XNLI, +13{\%} average F1 score on MLQA, and +2.4{\%} F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7{\%} in XNLI accuracy for Swahili and 11.4{\%} for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.",
}