Conclusiones de la evaluación de Modelos del Lenguaje en Español
ISSN: 1135-5948
Année de publication: 2023
Número: 70
Pages: 157-170
Type: Article
D'autres publications dans: Procesamiento del lenguaje natural
Résumé
Actualmente existen varios modelos del lenguaje en español (también conocidos como BERTs) los cuales han sido desarrollados tanto en el marco de grandes proyectos que utilizan corpus privados de gran tamaño, como mediante esfuerzos académicos de menor escala aprovechando datos de libre acceso. En este artículo presentamos una comparación exhaustiva de modelos de lenguaje en español con los siguientes resultados: (i) La inclusión de modelos multilingües previamente ignorados altera sustancialmente el panorama de la evaluación para el español, ya que resultan ser en general mejores que sus homólogos monolingües; (ii) Las diferencias en los resultados entre los modelos monolingües no son concluyentes, ya que aquellos supuestamente más pequeños e inferiores obtienen resultados más que competitivos. El resultado de nuestra evaluación demuestra que es necesario seguir investigando para comprender los factores que subyacen a estos resultados. En este sentido, es necesario seguir investigando el efecto del tamaño del corpus, su calidad y las técnicas de preentrenamiento para poder obtener modelos monolingües en español significativamente mejores que los multilingües ya existentes. Aunque esta actividad reciente demuestra un creciente interés en el desarrollo de la tecnología lingüística para el español, nuestros resultados ponen de manifiesto que el desarrollo de modelos de lenguaje sigue siendo un problema abierto que requiere conjugar recursos (monetarios y/o computacionales) con los mejores conocimientos y prácticas de investigación en PLN.
Références bibliographiques
- Agerri, R. 2020. Projecting heterogeneous annotations for named entity recognition. In IberLEF@SEPLN.
- Agerri, R., I. San Vicente, J. A. Campos, A. Barrena, X. Saralegi, A. Soroa, and E. Agirre. 2020. Give your Text Representation Models some Love: the Case for Basque. In LREC 2020, pages 4781{4788.
- Aghajanyan, A., A. Shrivastava, A. Gupta, N. Goyal, L. Zettlemoyer, and S. Gupta. 2021. Better ne-tuning by reducing representational collapse. In ICLR.
- Agirre, E., C. Banea, C. Cardie, D. M. Cer, M. T. Diab, A. Gonzalez-Agirre, W. Guo, I. Lopez-Gazpio, M. Maritxalar, R. Mihalcea, G. Rigau, L. Uria, and J. Wiebe. 2015. SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability. In SemEval@NAACL-HLT.
- Agirre, E., C. Banea, C. Cardie, D. M. Cer, M. T. Diab, A. Gonzalez-Agirre, W. Guo, R. Mihalcea, G. Rigau, and J. Wiebe. 2014. SemEval-2014 Task 10: Multilingual Semantic Textual Similarity. In SemEval.
- Armengol-Estape, J., C. P. Carrino, C. Rodriguez-Penagos, O. de Gibert Bonet, C. Armentano-Oller, A. Gonzalez-Agirre, M. Melero, and M. Villegas. 2021. Are multilingual models the best choice for moderately under-resourced languages? A comprehensive assessment for Catalan. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021.
- Artetxe, M., I. Aldabe, R. Agerri, O. P. de Vinaspre, and A. S. Etxabe. 2022. Does corpus quality really matter for lowresource languages? In EMNLP. Bhattacharjee, A., T. Hasan, K. Samin, M. S. Rahman, A. Iqbal, and R. Shahriyar. 2021. BanglaBERT: Combating Embedding Barrier for Low-Resource Language Understanding. In ArXiv, volume abs/2101.00204.
- Brown, T. B., B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei. 2020. Language models are few-shot learners. In arXiv, volume 2005.14165.
- Cañete, J., G. Chaperon, R. Fuentes, J.-H. Ho, H. Kang, and J. Perez. 2020. Spanish Pre-Trained BERT Model and Evaluation Data. In PML4DC at ICLR 2020.
- Clark, K., M.-T. Luong, Q. V. Le, and C. D. Manning. 2020. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. In ICLR.
- Conneau, A., K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzman, E. Grave, M. Ott, L. Zettlemoyer, and V. Stoyanov. 2020. Unsupervised Crosslingual Representation Learning at Scale. In ACL.
- Conneau, A., G. Lample, R. Rinott, A. Williams, S. R. Bowman, H. Schwenk, and V. Stoyanov. 2018. XNLI: Evaluating cross-lingual sentence representations. In EMNLP.
- De la Rosa, J., E. G. Ponferrada, M. Romero, P. Villegas, P. G. de Prado Salas, and M. Grandury. 2022. BERTIN: Ecient Pre-Training of a Spanish Language Model using Perplexity Sampling. Procesamiento del Lenguaje Natural, 68:13{23.
- de Vries, W., A. van Cranenburgh, A. Bisazza, T. Caselli, G. van Noord, and M. Nissim. 2019. BERTje: A Dutch BERT Model. In ArXiv, volume abs/1912.09582.
- Devlin, J., M. Chang, K. Lee, and K. Toutanova. 2019. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT, pages 4171{4186.
- Gutierrez-Fandino, A., J. ArmengolEstape, M. Pamies, J. Llop-Palao, J. Silveira-Ocampo, C. P. Carrino, C. Armentano-Oller, C. RodriguezPenagos, A. Gonzalez-Agirre, and M. Villegas. 2022. MarIA: Spanish Language Models. Procesamiento del Lenguaje Natural, 68:39{60.
- He, P., J. Gao, and W. Chen. 2021. DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing. In ArXiv, volume abs/2111.09543.
- Komatsuzaki, A. 2019. One Epoch Is All You Need. In ArXiv.
- Kreutzer, J., I. Caswell, L. Wang, A. Wahab, D. van Esch, N. Ulzii-Orshikh, A. Tapo, N. Subramani, A. Sokolov, C. Sikasote, M. Setyawan, S. Sarin, S. Samb, B. Sagot, C. Rivera, A. Rios, I. Papadimitriou, S. Osei, P. O. Suarez, I. Orife, K. Ogueji, A. N. Rubungo, T. Q. Nguyen, M. Muuller, A. Muuller, S. H. Muhammad, N. Muhammad, A. Mnyakeni, J. Mirzakhalov, T. Matangira, C. Leong, N. Lawson, S. Kudugunta, Y. Jernite, M. Jenny, O. Firat, B. F. P. Dossou, S. Dlamini, N. de Silva, S. C abuk Ball, S. Biderman, A. Battisti, A. Baruwa, A. Bapna, P. Baljekar, I. A. Azime, A. Awokoya, D. Ataman, O. Ahia, O. Ahia, S. Agrawal, and M. Adeyemi. 2022. Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets. Transactions of the Association for Computational Linguistics, 10:50{72.
- Lai, G., B. Oguz, Y. Yang, and V. Stoyanov. 2019. Bridging the domain gap in crosslingual document classication. In ArXiv, volume abs/1909.07009.
- Liu, Y., M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. In ArXiv, volume abs/1907.11692.
- Martin, L., B. Muller, P. J. Ortiz Suarez, Y. Dupont, L. Romary, E. de la Clergerie, D. Seddah, and B. Sagot. 2020. CamemBERT: a tasty French language model. In ACL.
- Nozza, D., F. Bianchi, and D. Hovy. 2020. What the [MASK]? Making Sense of Language-Specic BERT Models. In ArXiv, volume abs/2003.02912. Nzeyimana, A. and A. N. Rubungo. 2022. KinyaBERT: a Morphology-aware Kinyarwanda Language Model. In ACL.
- Ortiz Suarez, P. J., B. Sagot, and L. Romary. 2019. Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures. In P. Banski, A. Barbaresi, H. Biber, E. Breiteneder, S. Clematide, M. Kupietz, H. Luungen, and C. Iliadi, editors, Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardi, 22nd July 2019, pages 9{16.
- Otegi, A., A. Gonzalez-Agirre, J. A. Campos, A. S. Etxabe, and E. Agirre. 2020. Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque. In LREC.
- Pires, T. J. P., E. Schlinger, and D. Garrette. 2019. How Multilingual is Multilingual BERT? In ACL.
- Porta, J. and L. Espinosa-Anke. 2020. Overview of CAPITEL Shared Tasks at IberLEF 2020: Named Entity Recognition and Universal Dependencies Parsing. In IberLEF@SEPLN.
- Raffel, C., N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu. 2020. Exploring the limits of transfer learning with a unied texttotext transformer. Journal of Machine Learning Research, 21(140):1{67.
- Sanchez-Bayona, E. and R. Agerri. 2022. Leveraging a new spanish corpus for multilingual and crosslingual metaphor detection. In CoNLL.
- Scao, T. L., A. Fan, C. Akiki, E. Pavlick, S. Ilic, D. Hesslow, R. Castagne, A. S. Luccioni, F. Yvon, M. Galle, J. Tow, A. M. Rush, S. Biderman, A. Webson, P. S. Ammanamanchi, T. Wang, B. Sagot, N. Muennigho, A. V. del Moral, O. Ruwase, R. Bawden, S. Bekman, A. McMillan-Major, I. Beltagy, H. Nguyen, L. Saulnier, S. Tan, P. O.
- H. Rezanejad, H. Jones, I. Bhattacharya, I. Solaiman, I. Sedenko, I. Nejadgholi, J. Passmore, J. Seltzer, J. B. Sanz, K. Fort, L. Dutra, M. Samagaio, F. Barth, F. Fuhrimann, G. Altay, G. Bayrak, G. Burns, H. U. Vrabec, I. Bello, I. Dash, J. Kang, J. Giorgi, J. Golde, J. D. Posada, K. R. Sivaraman, L. Bulchandani, L. Liu, L. Shinzato, Y. Xu, Y. Xu, Z. Tan, Z. Xie, Z. Ye, M. Bras, Y. Belkada, and T. Wolf. 2022. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. In arXiv.
- Schwenk, H. and X. Li. 2018. A corpus for multilingual document classication in eight languages. In LREC.
- Straka, M., J. Strakova, and J. Hajic. 2019. Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing. In ArXiv, volume abs/1908.07448.
- Tanvir, H., C. Kittask, and K. Sirts. 2021. EstBERT: A Pretrained LanguageSpecic BERT for Estonian. In NODALIDA.
- Taule, M., M. A. Mart, and M. Recasens. 2008. AnCora: Multilevel Annotated Corpora for Catalan and Spanish. In LREC.
- Tiedemann, J. and S. Thottingal. 2020. OPUS-MT { Building open translation services for the World. In European Association for Machine Translation Conferences/Workshops.
- Tjong-Kim-Sang, E. 2002. Introduction to the CoNLL-2002 Shared Task: Language Independent Named Entity Recognition. In CoNLL.
- Urbizu, G., I. San Vicente, X. Saralegi, R. Agerri, and A. Soroa. 2022. BasqueGLUE: A natural language understanding benchmark for Basque. In LREC.
- Virtanen, A., J. Kanerva, R. Ilo, J. Luoma, J. Luotolahti, T. Salakoski, F. Ginter, and S. Pyysalo. 2019. Multilingual is not enough: BERT for Finnish. In ArXiv, volume abs/1912.07076.
- Wang, X., Y. Jiang, N. Bach, T. Wang, Z. Huang, F. Huang, and K. Tu. 2021. Automated concatenation of embeddings for structured prediction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).
- Wolf, T., L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, and J. Brew. 2020. Transformers: State-of-the-art natural language processing. In EMNLP.
- Wu, S. and M. Dredze. 2020. Are All Languages Created Equal in Multilingual BERT? In Workshop on Representation Learning for NLP.
- Xue, L., N. Constant, A. Roberts, M. Kale, R. Al-Rfou, A. Siddhant, A. Barua, and C. Rael. 2021. mT5: A massively multilingual pre-trained text-to-text transformer. In NAACL.
- Yang, Y., Y. Zhang, C. Tar, and J. Baldridge. 2019. PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identication. In EMNLP.
- Zhang, S., S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. V. Lin, T. Mihaylov, M. Ott, S. Shleifer, K. Shuster, D. Simig, P. S. Koura, A. Sridhar, T. Wang, and L. Zettlemoyer. 2022. Opt: Open pretrained transformer language models. In arXiv.
- Zheng, B., L. Dong, S. Huang, S. Singhal, W. Che, T. Liu, X. Song, and F. Wei. 2021. Allocating large vocabulary capacity for cross-lingual language model pretraining. In EMNLP.