Cover not available

In:Recent Advances in Multiword Units in Machine Translation and Translation Technology
Edited by Johanna Monti, Gloria Corpas Pastor, Ruslan Mitkov and Carlos Manuel Hidalgo-Ternero
[Current Issues in Linguistic Theory 366] 2024
► pp. 243–261

Get fulltext from our e-platform
JBE logo with link
PDF logo with download linkDownload Book PDF
EPUB logo with download linkDownload Book EPUB
authorization required

Chapter 13
Ontology-based formalisation of Italian clitic verbal MWEs

An approach for supporting machine translation

 | University of Naples “L’Orientale”
 | University of Naples “L’Orientale”
 | University of Naples “L’Orientale”
Published online: 7 November 2024
DOI logo with link
https://doi.org/10.1075/cilt.366.13dib
Abstract
In this paper we present the development of an ontology-based bilingual (IT-EN) lexicographic resource of Italian clitic Verbal MultiWord Expressions (VMWEs) to support machine translation. Starting from an analysis of these units and their linguistic features, we examine how Neural Machine Translation (NMT) handles complex VMWEs and the related translations issues. Finally, we propose a bilingual resource, formalised by means of the OntoLex-Lemon model, which accounts for morphological, syntactic, and semantic features of Italian clitic verbs, in order to enhance automatic translation of VMWEs.
Article outline
  • 1.Introduction
  • 2.Related work
  • 3.MWE formal representation in multilingual resources
  • 4.Clitic VMWE description
  • 5.Clitic VMWE translation issues
  • 6.Towards an ontology-based bilingual lexicographic resource
    • 6.1Description of linguistic features
    • 6.2OntoLex-Lemon formalisation
  • 7.Conclusion and future work
  • Notes
  • References
References (44)
References
Ahmadnia, B., Dorr, B. J., & Kordjamshidi, P. (2020). Knowledge graphs effectiveness in neural machine translation improvement. Computer Science, 21(3) Google Scholar logo with link to Google Scholar
Barreiro, A., & Batista, F. (2016, June). Machine translation of non-contiguous multiword units. Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing (pp. 22–30). ACL. Google Scholar logo with link to Google Scholar
Bergmanis, T., & Pinnis, M. (2021). Facilitating terminology translation with target lemma annotations. arXiv preprint arXiv:2101.10035. Google Scholar logo with link to Google Scholar
Bosque-Gil, J., Gracia, J., Montiel-Ponsoda, E., & Aguado-de-Cea, G. (2016, May). Modelling multilingual lexicographic resources for the web of data: The K dictionaries case. GLOBALEX 2016 Lexicographic Resources for Human Language Technology Workshop Programme (p. 65).
Calzolari, N., Bel, N., Choukri, K., Mariani, J., Monachini, M., Odijk, J., Piperidis, S., Quochi, V., & Soria, C. (2011). Final FLaReNet deliverable: Language resources for the future-The future of language resources. The Strategic Language Resource Agenda. FLaReNet project.Google Scholar logo with link to Google Scholar
Cardinaletti, A., & Starke, M. (1999). The typology of structural deficiency: On the three grammatical classes. In H. van Riemsdijk (Ed.), Clitics in the languages of Europe (pp. 145–233). Mouton de Gruyter.Google Scholar logo with link to Google Scholar
Cho, K., Van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014). On the properties of neural machine translation: Encoder-Decoder approaches. arXiv preprint arXiv:1409.1259. Google Scholar logo with link to Google Scholar
Colson, J. -P. (2019). Multi-word units in machine translation: Why the tip of the iceberg remains problematic – and a tentative corpus-driven solution. MUMTT 2019, the 4th Workshop on Multi-word Units in Machine Translation and Translation Technology. [URL].
Copestake, A., Lambeau, F., Villavicencio, A., Bond, F., Baldwin, T., Sag, I. A., & Flickinger, D. (2002, May). Multiword expressions: Linguistic precision and reusability. Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02). ACL.
Dardano, M., Trifone, P. (1985). La lingua italiana [The Italian Language]. Zanichelli.Google Scholar logo with link to Google Scholar
De Mauro, T. (1999). Introduzione [Introduction]. In GRADIT 1999-2007. UTET.Google Scholar logo with link to Google Scholar
Dougal, D. K., & Lonsdale, D. (2020, May). Improving NMT quality using terminology injection. In Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 4820–4827).
Espinal, M. T. (2009) Clitic incorporation and abstract semantic objects in idiomatic constructions. Linguistics, 47(6), 1221–1271. Google Scholar logo with link to Google Scholar
Fellbaum, C. (1998). A semantic network of English: The mother of all WordNets. EuroWordNet: A multilingual database with lexical semantic networks (pp. 137–148). Springer. Google Scholar logo with link to Google Scholar
Fillmore, C. J. (1977). Scenes-and-frames semantics. In A. Zampolli, (Ed.), Linguistic Structures Processing, number 59 in Fundamental Studies in Computer Science. North Holland.Google Scholar logo with link to Google Scholar
Francopoulo, G., Declerck, T., Monachini, M., & Romary, L. (2006). The relevance of standards for research infrastructure. Proc. LREC Workshop on Towards a Research Infrastructure for Language Resources, 2006
Francopoulo, G., Bel, N., George, M., Calzolari, N., Monachini, M., Pet, M., & Soria, C. (2009). Multilingual resources for NLP in the lexical markup framework (LMF). Language Resources and Evaluation, 43(1), 57–70. Google Scholar logo with link to Google Scholar
Hokamp, C., & Liu, Q. (2017). Lexically constrained decoding for sequence generation using grid beam search.. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers), (pp. 1535–1546). ACL.
Isabelle, P., Cherry, C., & Foster, G. (2017). A challenge set approach to evaluating machine translation. arXiv preprint arXiv:1704.07431. Google Scholar logo with link to Google Scholar
Ježek, E. (2005). Interazioni tra aspetto e diatesi nei verbi pronominali italiani [Interactions between aspect and diathesis in Italian pronominal verbs]. Studi di grammatica italiana, XXIII, 239–281.Google Scholar logo with link to Google Scholar
Kalchbrenner, N., & Blunsom, P. (2013, October). Recurrent continuous translation models. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1700–1709).
Khan, F., & Frontini, F. (2014, December). Publishing PAROLE SIMPLE CLIPS as linguistic linked open data. Proceedings of the First Italian Conference on Computational Linguistics CLiC-it 2014 (pp. 224–228).
Khan, F., Bellandi, A., Frontini, F., & Monachini, M. (2018). SWRL your lexicon: Adding inflectional rules to a LOD. The XVIII EURALEX International Congress (p. 68).
Kordoni, V., & Simova, I. (2014, May). Multiword expressions in machine translation. LREC 2014, Ninth International Conference on Language Resources and Evaluation (pp. 1208–1211).
Lu, Y., Zhang, J., & Zong, C. (2019). Exploiting knowledge graph in neural machine translation. In J. Chen & J. Zhang (Eds), Machine Translation. CWMT 2018. Communications in Computer and Information Science, Vol. 954 (pp. 27–38). Springer. Google Scholar logo with link to Google Scholar
Markantonatou, S., Ramisch, C., Savary, A., & Vincze, V. (2018). Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop. Language Science Press.Google Scholar logo with link to Google Scholar
Masini, F. (2015, May). Idiomatic verb-clitic constructions: Lexicalization and productivity. Mediterranean Morphology Meetings (Vol. 9, pp. 88–104).Google Scholar logo with link to Google Scholar
McCrae, J., Aguado-de-Cea, G., Buitelaar, P., Cimiano, P., Declerck, T., Gómez-Pérez, A., … & Wunner, T. (2012). Interchanging lexical resources on the semantic web. Language Resources and Evaluation, 46(4), 701–719. Google Scholar logo with link to Google Scholar
McCrae, J. P., Bosque-Gil, J., Gracia, J., Buitelaar, P., & Cimiano, P. (2017, September). The Ontolex-Lemon model: Development and applications. Proceedings of eLex 2017 conference (pp. 19–21).
McCrae, J. P., Cimiano, P., Buitelaar, P., & Bordea, G. (2016). Representing multiword expressions on the Web with the OntoLex-Lemon model. In PARSEME/ENeL workshop on MWE e-lexicons.
Monti, J., Barreiro, A., Elia, A., Marano, F., & Napoli, A. (2011). Taking on new challenges in multi-word unit processing for machine translation. In Second International Workshop on Free/Open-Source Rule-Based Machine Translation (pp. 11–19). UOC. EDU.Google Scholar logo with link to Google Scholar
Monti, J., & di Buono, M. P. (2019). PARSEME-It: An Italian corpus annotated with verbal multiword expressions. IJCoL. Italian Journal of Computational Linguistics, 5(5–2), 61–93. Google Scholar logo with link to Google Scholar
Moussallem, D., Ngonga Ngomo, A. C., Buitelaar, P., & Arcan, M. (2019, September). Utilizing knowledge graphs for neural machine translation augmentation. Proceedings of the 10th International Conference on Knowledge Capture, K-CAP ’19 (pp. 139–146). New York, NY, USA. Association for Computing Machinery.
Ortiz, A. M. (2000). Diseño e implementación de un lexicón computacional para la lexicografía y la traducción automática [Project and development of a computational lexicon for lexicography and automatic translation] [Doctoral dissertation, Universidad de Córdoba].
Ramisch, C., Besacier, L., & Kobzar, A. (2013, September). How hard is it to automatically translate phrasal verbs from English to French. MT Summit 2013 Workshop on Multi-word Units in Machine Translation and Translation Technology (pp. 53–61).
Ramisch, C., Cordeiro, S., Savary, A., Vincze, V., Mititelu, V., Bhatia, A., Buljan M., Candito, M., Gantar, P., Giouli, V., Güngör, T., Hawwari, A., Iñurrieta, U., Kovalevskaite, J., Krek, S., Lichte, T., Liebeskind, C., Monti, J. Parra Escartín, C., QasemiZadeh, B., Ramisch, R., Schneider N., Stoyanova, I. Vaidya, A. Walsh, A., & Güngör, T. (2018, August). Edition 1.1 of the PARSEME Shared Task on automatic identification of verbal multiword expressions. Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE‑CxG-2018), ACL (pp. 222–240).Google Scholar logo with link to Google Scholar
Salvi, G. (1988). La frase semplice [Simple sentence]. In Renzi, L. (ed.), Grande grammatica di consultazione, Vol. 1, 29–114. Il Mulino.Google Scholar logo with link to Google Scholar
Savary, A., Ramisch, C., Cordeiro, S. R., Sangati, F., Vincze, V., Qasemi Zadeh, B., Candito, M, Cap, F., Giouli, V., Stoyanova, I. & Doucet, A. (2017). The PARSEME Shared Task on automatic identification of verbal multiword expressions. Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017) (pp. 31–47). ACL. Google Scholar logo with link to Google Scholar
Serianni, L. (2006). Grammatica italiana. Italiano comune e lingua letteraria [Italian grammar: Common Italian and literary language]. UTET.Google Scholar logo with link to Google Scholar
Song, K., Wang, K., Yu, H., Zhang, Y., Huang, Z., Luo, W., Duan, X., & Zhang, M. (2020, April). Alignment-enhanced transformer for constraining nmt with pre-specified translations. Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), 8886–8893.
Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems (pp. 3104–3112).
Toral, A., & Monachini, M. (2007). Simpleowl: A generative lexicon ontology for NLP and the semantic web. Workshop on Cooperative Construction of Linguistic Knowledge Bases (AIIA 2007).
Zaninello, A., & Birch, A. (2020, May). Multiword expression aware neural machine translation. Proceedings of The 12th Language Resources and Evaluation Conference (pp. 3816–3825). European Language Resources Association.
Zhao, Y., Xiang, L., Zhu, J., Zhang, J., Zhou, Y., & Zong, C. (2020, December). Knowledge graph enhanced neural machine translation via multi-task learning on sub-entity granularity. Proceedings of the 28th International Conference on Computational Linguistics (pp. 4495–4505). Online. International Committee on Computational Linguistics.
Mobile Menu Logo with link to supplementary files background Layer 1 prag Twitter_Logo_Blue