@inproceedings{teslenko-grygoryev-etal-2022-english,
    title = "{E}nglish-{R}ussian Data Augmentation for Neural Machine Translation",
    author = "Teslenko Grygoryev, Nikita  and
      Garcia Martinez, Mercedes  and
      Casacuberta Nolla, Francisco  and
      Estela Pastor, Amando  and
      Herranz, Manuel",
    editor = "Ortega, John E.  and
      Carpuat, Marine  and
      Chen, William  and
      Kann, Katharina  and
      Lignos, Constantine  and
      Popovic, Maja  and
      Tafreshi, Shabnam",
    booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 2: Corpus Generation and Corpus Augmentation for Machine Translation)",
    month = sep,
    year = "2022",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2022.amta-coco4mt.1",
    pages = "1--10",
    abstract = "Data Augmentation (DA) refers to strategies for increasing the diversity of training examples without explicitly collecting new data manually. We have used neural networks and linguistic resources for the automatic generation of text in Russian. The system generates new texts using information from embeddings trained with a huge amount of data in neural language models. Data from the public domain have been used for experiments. The generation of these texts increases the corpus used to train models for NLP tasks, such as machine translation. Finally, an analysis of the results obtained evaluating the quality of generated texts has been carried out and those texts have been added to the training process of Neural Machine Translation (NMT) models. In order to evaluate the quality of the NMT models, firstly, these models have been compared performing a quantitative analysis by means of several standard automatic metrics used in machine translation, and measuring the time spent and the amount of text generated for a good use in the language industry. Secondly, NMT models have been compared through a qualitative analysis, where generated examples of translation have been exposed and compared with each other. Using our DA method, we achieve better results than a baseline model by fine tuning NMT systems with the newly generated datasets.",
}