{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:05:17Z","timestamp":1743030317449,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030898199"},{"type":"electronic","value":"9783030898205"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89820-5_10","type":"book-chapter","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T20:35:26Z","timestamp":1634762126000},"page":"120-139","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Nahuatl Neural Machine Translation Using Attention Based Architectures: A\u00a0Comparative Analysis for RNNs and\u00a0Transformers as a Mobile Application\u00a0Service"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5641-7185","authenticated-orcid":false,"given":"Sergio Khalil","family":"Bello Garc\u00eda","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6875-7670","authenticated-orcid":false,"given":"Eduardo","family":"S\u00e1nchez Lucero","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2062-4219","authenticated-orcid":false,"given":"Edmundo","family":"Bonilla Huerta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7245-9564","authenticated-orcid":false,"given":"Jos\u00e9 Crisp\u00edn","family":"Hern\u00e1ndez Hern\u00e1ndez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4468-4171","authenticated-orcid":false,"given":"Jos\u00e9 Federico","family":"Ram\u00edrez Cruz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9819-635X","authenticated-orcid":false,"given":"Blanca Estela","family":"Pedroza M\u00e9ndez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","first-page":"253","DOI":"10.22158\/sll.v3n3p253","volume":"3","author":"M Aiken","year":"2019","unstructured":"Aiken, M.: An updated evaluation of Google translate accuracy. Stud. Linguist. Lit. 3, 253 (2019). https:\/\/doi.org\/10.22158\/sll.v3n3p253","journal-title":"Stud. Linguist. Lit."},{"unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. ArXiv 1409, September 2014","key":"10_CR2"},{"unstructured":"Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 1171\u20131179. Curran Associates, Inc. (2015). https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/e995f98d56967d946471af29d7bf99f1-Paper.pdf","key":"10_CR3"},{"unstructured":"Carolina, E., Cerb\u00f3n, V., Gutierrez-vasques, X.: Recopilaci\u00f3n de un corpus paralelo electr\u00f3nico para una lengua minoritaria: el caso del espa\u00f1ol-n\u00e1huatl, January 2015","key":"10_CR4"},{"doi-asserted-by":"crossref","unstructured":"Charoenpornsawat, P., Sornlertlamvanich, V., Charoenporn, T.: Improving translation quality of rule-based machine translation. In: COLING-2002: Machine Translation in Asia (2002)","key":"10_CR5","DOI":"10.3115\/1118794.1118799"},{"unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs\/1810.04805 (2018). http:\/\/arxiv.org\/abs\/1810.04805","key":"10_CR6"},{"unstructured":"Eberhard, D.M., Simons, G.F., Fennig, C.D.: Ethnologue: Languages of the World, 23 edn. SIL International, Dallas (2020). https:\/\/www.ethnologue.com\/language\/nhe","key":"10_CR7"},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"41","DOI":"10.26342\/2019-63-4","volume":"63","author":"X Gutierrez-vasques","year":"2019","unstructured":"Gutierrez-vasques, X., Medina-Urrea, A., Sierra, G.: Morphological segmentation for extracting Spanish-Nahuatl bilingual lexicon. Procesamiento de Lenguaje Natural 63, 41\u201348 (2019). https:\/\/doi.org\/10.26342\/2019-63-4","journal-title":"Procesamiento de Lenguaje Natural"},{"unstructured":"Gutierrez-Vasques, X., Sierra, G., Pompa, I.H.: Axolotl: a web accessible parallel corpus for Spanish-Nahuatl. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pp. 4210\u20134214. European Language Resources Association (ELRA), Portoro\u017e, May 2016. https:\/\/www.aclweb.org\/anthology\/L16-1666","key":"10_CR9"},{"unstructured":"Gutierrez-Vasques, X., Sierra, G., Pompa, I.H.: Axolotl corpus paralelo n\u00e1huatl-espa\u00f1ol beta (2020). https:\/\/axolotl-corpus.mx\/search","key":"10_CR10"},{"unstructured":"Instituto Nacional de Antropolog\u00eda e Historia (Mexico): CEN juntamente: compendio enciclop\u00e9dico del N\u00e1huatl. Instituto Nacional de Antropolog\u00eda e Historia (2009). https:\/\/books.google.com.mx\/books?id=JccvxgEACAAJ","key":"10_CR11"},{"issue":"2","key":"10_CR12","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3390\/info11020108","volume":"11","author":"J Howard","year":"2020","unstructured":"Howard, J., Gugger, S.: Fastai: a layered API for deep learning. Information 11(2), 108 (2020). https:\/\/doi.org\/10.3390\/info11020108","journal-title":"Information"},{"unstructured":"Microsoft Inc.: Microsoft translator community partners (2016). https:\/\/www.microsoft.com\/en-us\/translator\/business\/community\/","key":"10_CR13"},{"doi-asserted-by":"crossref","unstructured":"Klein, G., Kim, Y., Deng, Y., Senellart, J., Rush, A.M.: OpenNMT: open-source toolkit for neural machine translation. CoRR abs\/1701.02810 (2017). http:\/\/arxiv.org\/abs\/1701.02810","key":"10_CR14","DOI":"10.18653\/v1\/P17-4012"},{"unstructured":"Koehn, P.: Statistical significance tests for machine translation evaluation, pp. 388\u2013395, January 2004","key":"10_CR15"},{"key":"10_CR16","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511815829","volume-title":"Statistical Machine Translation","author":"P Koehn","year":"2009","unstructured":"Koehn, P.: Statistical Machine Translation. Cambridge University Press, Cambridge (2009)"},{"doi-asserted-by":"crossref","unstructured":"Kudo, T.: Subword regularization: improving neural network translation models with multiple subword candidates. CoRR abs\/1804.10959 (2018). http:\/\/arxiv.org\/abs\/1804.10959","key":"10_CR17","DOI":"10.18653\/v1\/P18-1007"},{"doi-asserted-by":"crossref","unstructured":"Kudo, T., Richardson, J.: SentencePiece: a simple and language independent subword tokenizer and detokenizer for neural text processing. CoRR abs\/1808.06226 (2018). http:\/\/arxiv.org\/abs\/1808.06226","key":"10_CR18","DOI":"10.18653\/v1\/D18-2012"},{"unstructured":"SIL International (formerly known as the Summer Institute of Linguistics): ISO 639 code tables (2020). https:\/\/iso639-3.sil.org\/code_tables\/639\/data\/n?name_3=nahuatl","key":"10_CR19"},{"doi-asserted-by":"publisher","unstructured":"Liu, Q., Wang, J., Zhang, D., Yang, Y., Wang, N.: Text features extraction based on TF-IDF associating semantic. In: 2018 IEEE 4th International Conference on Computer and Communications (ICCC), pp. 2338\u20132343, December 2018. https:\/\/doi.org\/10.1109\/CompComm.2018.8780663","key":"10_CR20","DOI":"10.1109\/CompComm.2018.8780663"},{"doi-asserted-by":"crossref","unstructured":"Luong, M., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. CoRR abs\/1508.04025 (2015). http:\/\/arxiv.org\/abs\/1508.04025","key":"10_CR21","DOI":"10.18653\/v1\/D15-1166"},{"unstructured":"Mager, M., Gutierrez-Vasques, X., Sierra, G., Meza-Ru\u00edz, I.V.: Challenges of language technologies for the indigenous languages of the Americas. CoRR abs\/1806.04291 (2018). http:\/\/arxiv.org\/abs\/1806.04291","key":"10_CR22"},{"unstructured":"Mager, M., Meza, I.: Hacia la traducci\u00f3n autom\u00e1tica de las lenguas ind\u00edgenas de m\u00e9xico, June 2018","key":"10_CR23"},{"doi-asserted-by":"publisher","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation, October 2002. https:\/\/doi.org\/10.3115\/1073083.1073135","key":"10_CR24","DOI":"10.3115\/1073083.1073135"},{"unstructured":"TheWordPoint: What is the most translated website in the world? (2020). https:\/\/thewordpoint.com\/blog\/worlds-most-translated-website","key":"10_CR25"},{"unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2018). https:\/\/d4mucfpksywv.cloudfront.net\/better-language-models\/language-models.pdf","key":"10_CR26"},{"unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. CoRR abs\/1910.10683 (2019). http:\/\/arxiv.org\/abs\/1910.10683","key":"10_CR27"},{"unstructured":"R\u00edos Dolores, J.C., Sierra Mart\u00ednez, G.E.: Traducci\u00f3n autom\u00e1tica n\u00e1huatl-espa\u00f1ol: variables que influyen en la calidad de la traducci\u00f3n. Master\u2019s thesis, Universidad Nacional Aut\u00f3noma de M\u00e9xico, September 2019. http:\/\/132.248.9.195\/ptd2019\/septiembre\/0795765\/Index.html","key":"10_CR28"},{"issue":"2","key":"10_CR29","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1023\/A:1008109312730","volume":"14","author":"H Somers","year":"1999","unstructured":"Somers, H.: Example-based machine translation. Mach. Transl. 14(2), 113\u2013157 (1999). https:\/\/doi.org\/10.1023\/A:1008109312730","journal-title":"Mach. Transl."},{"unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. CoRR abs\/1409.3215 (2014). http:\/\/arxiv.org\/abs\/1409.3215","key":"10_CR30"},{"unstructured":"Thouvenot, M.: Gran diccionario n\u00e1huatl (2005). http:\/\/www.gdn.unam.mx\/","key":"10_CR31"},{"unstructured":"Vaswani, A., et al.: Attention is all you need. CoRR abs\/1706.03762 (2017). http:\/\/arxiv.org\/abs\/1706.03762","key":"10_CR32"},{"unstructured":"Villegas, M.R.: Diccionario aulex n\u00e1huatl espa\u00f1ol, April 2019. https:\/\/aulex.org\/ayuda\/nahuatl.php","key":"10_CR33"},{"unstructured":"Virpioja, S., Smit, P., Gr\u00f6nroos, S., Kurimo, M.: Morfessor 2.0: Python implementation and extensions for Morfessor Baseline (2013)","key":"10_CR34"},{"issue":"2","key":"10_CR35","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","volume":"1","author":"RJ Williams","year":"1989","unstructured":"Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270\u2013280 (1989). https:\/\/doi.org\/10.1162\/neco.1989.1.2.270","journal-title":"Neural Comput."},{"unstructured":"Witnesses, J.: How is our literature written and translated? (2021). https:\/\/www.jw.org\/en\/library\/books\/jehovahs-will\/literature-written-and-translated\/","key":"10_CR36"},{"unstructured":"Wu, Y., et al.: Google\u2019s neural machine translation system: bridging the gap between human and machine translation, September 2016","key":"10_CR37"},{"doi-asserted-by":"crossref","unstructured":"Zhang, S., Frey, B., Bansal, M.: ChrEn: Cherokee-English machine translation for endangered language revitalization (2020)","key":"10_CR38","DOI":"10.18653\/v1\/2020.emnlp-main.43"}],"container-title":["Lecture Notes in Computer Science","Advances in Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89820-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:58:35Z","timestamp":1634864315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89820-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030898199","9783030898205"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89820-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"129","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}