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Multilingual extension and evaluation of a poetry generator*

Published online by Cambridge University Press:  01 June 2017

HUGO GONÇALO OLIVEIRA Open the ORCID record for HUGO GONÇALO OLIVEIRA [Opens in a new window] ,
RAQUEL HERVÁS ,
ALBERTO DÍAZ  and
PABLO GERVÁS
HUGO GONÇALO OLIVEIRA
Affiliation:
CISUC, Dep. Engenharia Informática, Universidade de Coimbra, Portugal e-mail: [email protected]
RAQUEL HERVÁS
Affiliation:
Dep. de Ing. de Software e Int. Artificial, Universidad Complutense de Madrid, Spain e-mail: [email protected], [email protected]
ALBERTO DÍAZ
Affiliation:
Dep. de Ing. de Software e Int. Artificial, Universidad Complutense de Madrid, Spain e-mail: [email protected], [email protected]
PABLO GERVÁS
Affiliation:
Dep. de Ing. de Software e Int. Artificial, Universidad Complutense de Madrid, Spain e-mail: [email protected], [email protected] Inst. de Tecnología del Conocimiento, Universidad Complutense de Madrid, Spain e-mail: [email protected]
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Abstract

Poetry generation is a specific kind of natural language generation where several sources of knowledge are typically exploited to handle features on different levels, such as syntax, semantics, form or aesthetics. But although this task has been addressed by several researchers, and targeted different languages, all known systems have focused on a limited purpose and a single language. This article describes the effort of adapting the same architecture to generate poetry in three different languages – Portuguese, Spanish and English. An existing architecture is first described and complemented with the adaptations required for each language, including the linguistic resources used for handling morphology, syntax, semantics and metric scansion. An automatic evaluation was designed in such a way that it would be applicable to the target languages. It covered three relevant aspects of the generated poems, namely: the presence of poetic features, the variation of the linguistic structure and the semantic connection to a given topic. The automatic measures applied for the second and third aspect can be seen as novel in the evaluation of poetry. Overall, poems were successfully generated in the three languages addressed. Despite minor differences in different languages or seed words, poems revealed to have a regular metre, frequent rhymes, to exhibit an interesting degree of variation, and to be semantically-associated with the initially given seeds.

Type
Articles
Information
Natural Language Engineering , Volume 23 , Issue 6 , November 2017 , pp. 929 - 967
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

*

This work was supported by projects PROSECCO and ConCreTe. Part of this work was developed during short term visits funded by the PROSECCO CSA project, European Commission under FP7 FET grant number 600653. The project ConCreTe acknowledges the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET grant number 611733.

References

Aamodt, A., and Plaza, E. 1994. Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Communications 7 (1): 39–59.CrossRefGoogle Scholar
Agirrezabal, M., Arrieta, B., Astigarraga, A., and Hulden, M. 2013. POS-tag based poetry generation with WordNet. In Proceedings of the 14th European Workshop on Natural Language Generation, Sofia, Bulgaria, pp. 162–6. ACL Press.Google Scholar
Barbieri, G., Pachet, F., Roy, P., and Esposti, M. D. 2012. Markov constraints for generating lyrics with style. In Proceedings of 20th European Conference on Artificial Intelligence (ECAI), Frontiers in Artificial Intelligence and Applications, vol. 242, pp. 115–20. IOS Press.Google Scholar
Bouma, G. 2009. Normalized (pointwise) mutual information in collocation extraction. In Proceedings of the Biennial GSCL Conference, Gunter Narr Verlag, pp. 31–40.Google Scholar
Brin, S., and Page, L. 1998. The anatomy of a large-scale hypertextual web search engine. Computer Networks 30 (1–7): 107–17.Google Scholar
Charnley, J., Colton, S., and Llano, M. T. 2014. The FloWr framework: automated flowchart construction, optimisation and alteration for creative systems. In Proceedings of 5th International Conference on Computational Creativity, ICCC 2014, Ljubljana, Slovenia.Google Scholar
Charnley, J., Pease, A., and Colton, S., 2012. On the notion of framing in computational creativity. In Proceedings of the 3rd International Conference on Computational Creativity, ICCC 2012, Dublin, Ireland, pp. 77–81.Google Scholar
Church, K. W., and Hanks, P. 1990. Word association norms, mutual information, and lexicography. Computational Linguistics 16 (1): 22–9.Google Scholar
Colton, S., Goodwin, J., and Veale, T., 2012. Full FACE poetry generation. In Proceedings of 3rd International Conference on Computational Creativity, ICCC 2012, Dublin, Ireland, pp. 95–102.Google Scholar
Colton, S., and Wiggins, G. A. 2012. Computational creativity: The final frontier? In Proceedings of 20th European Conference on Artificial Intelligence (ECAI 2012), Frontiers in Artificial Intelligence and Applications, vol. 242, Montpellier, France, pp. 21–6. IOS Press.Google Scholar
Das, A., and Gambäck, B. 2014. Poetic machine: computational creativity for automatic poetry generation in Bengali. In Proceedings of 5th International Conference on Computational Creativity, ICCC 2014, Ljubljana, Slovenia.Google Scholar
Fellbaum, C. (ed.), 1998. WordNet: An Electronic Lexical Database (Language, Speech, and Communication). Cambridge, MA: The MIT Press.CrossRefGoogle Scholar
Gervás, P., 2000a. WASP: evaluation of different strategies for the automatic generation of Spanish verse. In Proceedings of AISB’00 Symposium on Creative & Cultural Aspects and Applications of AI & Cognitive Science, Birmingham, UK, pp. 93–100.Google Scholar
Gervás, P., 2000b. A logic programming application for the analysis of Spanish verse. In Proceedings of the 1st International Conference on Computational Logic, Imperial College, London, UK, pp. 1330–44.Google Scholar
Gervás, P. 2001. An expert system for the composition of formal Spanish poetry. Journal of Knowledge-Based Systems 14 (3–4): 181–8.CrossRefGoogle Scholar
Gervás, P. 2013a. Computational modelling of poetry generation. In Proceedings of the AISB’13 Symposium on Artificial Intelligence and Poetry, University of Exeter, Exeter, UK.Google Scholar
Gervás, P. 2013b. Evolutionary elaboration of daily news as a poetic stanza. In Proceedings of the IX Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados - MAEB 2013, University of Exeter, Exeter, UK.Google Scholar
Gonçalo Oliveira, H. 2012. PoeTryMe: a versatile platform for poetry generation. In Proceedings of the ECAI 2012 Workshop on Computational Creativity, Concept Invention, and General Intelligence, C3GI 2012, Montpellier, France.Google Scholar
Gonçalo Oliveira, H. 2015. Tra-la-Lyrics 2.0: automatic generation of song lyrics on a semantic domain. Journal of Artificial General Intelligence 6 (1): 87–110. Special Issue: Computational Creativity, Concept Invention, and General Intelligence.CrossRefGoogle Scholar
Gonçalo Oliveira, H., 2016. Automatic generation of poetry inspired by Twitter trends. In Knowledge Discovery, Knowledge Engineering and Knowledge Management (Post-conference Proceedings of IC3K — Revised Selected Papers), CCIS, vol. 631, Springer, pp. 13–27.CrossRefGoogle Scholar
Gonçalo Oliveira, H., Antón Pérez, L., Costa, H., and Gomes, P. 2011. Uma rede léxico-semântica de grandes dimensões para o português, extraída a partir de dicionários electrónicos. Linguamática 3 (2): 23–38.Google Scholar
Gonçalo Oliveira, H., and Cardoso, A. 2015. Poetry generation with PoeTryMe. In Besold, T. R., Schorlemmer, M., and Smaill, A. (eds.), Computational Creativity Research: Towards Creative Machines, Atlantis Thinking Machines, Chapter 12, pp. 243–66. Paris, France: Atlantis-Springer.CrossRefGoogle Scholar
Gonçalo Oliveira, H., Cardoso, F. A., and Pereira, F. C., 2007. Tra-la-Lyrics: an approach to generate text based on rhythm. In Proceedings of 4th International Joint Workshop on Computational Creativity, London, UK, IJWCC 2007, pp. 47–55.Google Scholar
Gonçalo Oliveira, H., Hervás, R., Díaz, A., and Gervás, P. 2014. Adapting a generic platform for poetry generation to produce Spanish poems. In Proceedings of 5th International Conference on Computational Creativity, ICCC 2014, Ljubljana, Slovenia.Google Scholar
Gonçalo Oliveira, H., and Oliveira Alves, A. 2016. Poetry from concept maps – yet another adaptation of PoeTryMe’s flexible architecture. In Proceedings of 7th International Conference on Computational Creativity, ICCC 2016, Paris, France.Google Scholar
Gonzalez-Agirre, A., Laparra, E., and Rigau, G. 2012, LREC’12. Multilingual Central Repository version 3.0. In Proceedings of the 8th International Conference on Language Resources and Evaluation, ELRA, Istanbul, Turkey, pp. 2525–9.Google Scholar
Jordanous, A. 2012. A standardised procedure for evaluating creative systems: computational creativity evaluation based on what it is to be creative. Cognitive Computation 4 (3): 246–79.CrossRefGoogle Scholar
Lamb, C. E., Brown, D. G., and Clarke, C. L. 2015. Can human assistance improve a computational poet? In Proceedings of Bridges 2015: Mathematics, Music, Art, Architecture, Culture, Phoenix, Arizona, Tessellations Publishing, pp. 37–44.Google Scholar
Lamb, C., Brown, D., and Clarke, C. 2016. How digital poetry experts evaluate digital poetry. In Proceedings of 7th International Conference on Computational Creativity, ICCC 2016, Paris, France.Google Scholar
Lin, C.-Y., and Och, F. J. 2004. Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, ACL 2004, Stroudsburg, PA, USA: ACL Press.Google Scholar
Liu, B., Hu, M., and Cheng, J., 2005. Opinion observer: analyzing and comparing opinions on the Web. In Proceedings of the 14th International Conference on World Wide Web, WWW '05, New York, NY, USA, ACM, pp. 342–51.CrossRefGoogle Scholar
Malmi, E., Takala, P., Toivonen, H., Raiko, T., and Gionis, A., 2016. DopeLearning: a computational approach to rap lyrics generation. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13–17, 2016, pp. 195–204.CrossRefGoogle Scholar
Manurung, H. 1999. A chart generator for rhythm patterned text. In Proceedings of 1st International Workshop on Literature in Cognition and Computer, Tokyo, Japan.Google Scholar
Manurung, H. M., 2003. An Evolutionary Algorithm Approach to Poetry Generation. Ph D Thesis, UK: University of Edimburgh.Google Scholar
Misztal, J., and Indurkhya, B. 2014. Poetry generation system with an emotional personality. In Proceedings of the 5th International Conference on Computational Creativity, ICCC 2014, Ljubljana, Slovenia.Google Scholar
Netzer, Y., Gabay, D., Goldberg, Y., and Elhadad, M., 2009. Gaiku: generating haiku with word associations norms. In Proceedings of the Workshop on Computational Approaches to Linguistic Creativity, CALC’09, Stroudsburg, PA, USA: ACL Press, pp. 32–9.CrossRefGoogle Scholar
Newman, D., Lau, J. H., Grieser, K., and Baldwin, T., 2010. Automatic evaluation of topic coherence. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT '10, Stroudsburg, PA, USA: ACL Press, pp. 100–8.Google Scholar
Oulipo, A. 1981. Atlas de Littérature Potentielle, Collection Idées, vol. 1. Gallimard.Google Scholar
Padró, L., and Stanilovsky, E. 2012. Freeling 3.0: towards wider multilinguality. In Proceedings of the Language Resources and Evaluation Conference, LREC’12, Istanbul, Turkey, ELRA.Google Scholar
Pease, A., and Colton, S. 2011. On impact and evaluation in computational creativity: a discussion of the turing test and an alternative proposal. In Proceedings of the 3rd AISB Symposium on AI and Philosophy, UK: University of York.Google Scholar
Queneau, R. 1961. 100.000.000.000.000 de poèmes. Gallimard Series. Schoenhof’s Foreign Books, Incorporated.Google Scholar
Ramakrishnan, A. A., Kuppan, S., and Devi, S. L., 2009. Automatic generation of Tamil lyrics for melodies. In Proceedings of the Workshop on Computational Approaches to Linguistic Creativity, CALC’09, Stroudsburg, PA, USA: ACL Press, pp. 40–6.CrossRefGoogle Scholar
Ranchhod, E., Mota, C., and Baptista, J., 1999. A computational lexicon of Portuguese for automatic text parsing. In Proceedings of SIGLEX99: Standardizing Lexical Resources – 37th Annual Meeting of the ACL, College Park, MD, USA, ACL Press, pp. 74–80.Google Scholar
Rashel, F., and Manurung, R. 2014. Pemuisi: a constraint satisfaction-based generator of topical indonesian poetry. In Proceedings of 5th International Conference on Computational Creativity, ICCC 2014, Ljubljana, Slovenia.Google Scholar
Reiter, E., and Dale, R., 2000. Building Natural Language Generation Systems. New York, USA: Cambridge University Press.CrossRefGoogle Scholar
Santos, D., and Bick, E. 2000. Providing internet access to portuguese corpora: the AC/DC project. In Proceedings of 2nd International Conference on Language Resources and Evaluation, LREC 2000, ELRA, pp. 205–10, Athens, Greece.Google Scholar
Silva, M. J., Carvalho, P., and Sarmento, L., 2012. Building a sentiment lexicon for social judgement mining. In Proceedings of Computational Processing of the Portuguese Language – 10th International Conference (PROPOR 2012), LNCS, vol. 7243, Coimbra, Portugal, Springer, pp. 218–28.CrossRefGoogle Scholar
Tobing, B. C. L., and Manurung, R. 2015. A chart generation system for topical metrical poetry. In Proceedings of the 6th International Conference on Computational Creativity, Park City, Utah, USA, ICCC 2015, Park City, Utah, USA.Google Scholar
Toivanen, J. M., Gross, O., and Toivonen, H. 2014. The officer is taller than you, who race yourself! Using document specific word associations in poetry generation. In Proceedings of the 5th International Conference on Computational Creativity, ICCC 2014, Ljubljana, Slovenia.Google Scholar
Toivanen, J. M., Järvisalo, M., and Toivonen, H., 2013. Harnessing constraint programming for poetry composition. In Proceedings of the 4th International Conference on Computational Creativity, ICCC 2013, The University of Sydney, pp. 160–7.Google Scholar
Toivanen, J. M., Toivonen, H., Valitutti, A., and Gross, O. 2012. Corpus-based generation of content and form in poetry. In Proceedings of the 3rd International Conference on Computational Creativity, ICCC 2012, pp. 211–5, Dublin, Ireland.Google Scholar
Turney, P. D., 2001. Mining the web for synonyms: PMI–IR versus LSA on TOEFL. In Proceedings of 12th European Conference on Machine Learning, ECML 2001, LNCS, vol. 2167, London, UK: Springer, pp. 491–502.CrossRefGoogle Scholar
Urizar, X. S., and Roncal, I. S. V. 2013. Elhuyar at TASS 2013. In Proceedings of XXIX Congreso de la Sociedad Española de Procesamiento de lenguaje natural. Workshop on Sentiment Analysis at SEPLN (TASS2013), Madrid, pp. 143–50.Google Scholar
Valitutti, A., Doucet, A., Toivanen, J., and Toivonen, H. 2016. Computational generation and dissection of lexical replacement humor. Natural Language Engineering 22 (5): 1–23.CrossRefGoogle Scholar
van der Velde, F., Wolf, R. A., Schmettow, M., and Nazareth, D. S. 2015. A semantic map for evaluating creativity. In Proceedings of the 6th International Conference on Computational Creativity June, p. 94, Sony {CSL} Paris, France.Google Scholar
Veale, T., 2012. Exploding The Creativity Myth: The Computational Foundations of Linguistic Creativity. London, UK: Bloomsbury Publishing.Google Scholar
Wong, M. T., and Chun, A. H. W. 2008. Automatic haiku generation using VSM. In Proceeding of 7th WSEAS International Conference on Applied Computer & Applied Computational Science, ACACOS '08, Hangzhou, China.Google Scholar
Yan, R., Jiang, H., Lapata, M., Lin, S.-D., Lv, X., and Li, X., 2013. I, poet: automatic chinese poetry composition through a generative summarization framework under constrained optimization. In Proceedings of 23rd International Joint Conference on Artificial Intelligence, IJCAI’13, Palo Alto, California: AAAI Press, pp. 2197–203.Google Scholar
Zhang, X., and Lapata, M., 2014. Chinese poetry generation with recurrent neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, Qatar, Stroudsburg, PA, USA: ACL Press, pp. 670–80.CrossRefGoogle Scholar