Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data learned from character-based neural models are more informative than the ones from hand-crafted features, and that a Bi-LSTM+CRF-model produces state-of-the art accuracy on scansion of poetry in two languages. Results also show that the information about whole word structure, and not just independent syllables, is highly informative for performing scansion.

Keywords: scansion, English, poetry, out-of-vocabulary wordsWe present a finite-state technology (FST) based system capable of performing metrical scansion of verse written in English. Scansion is the traditional task of analyzing the lines of a poem, marking the stressed and non-stressed elements and dividing the line into metrical feet. The system's workflow is composed of several subtasks designed around finite-state machines that analyze verse by performing tokenization, part-of-speech tagging, stress placement, and stress-pattern prediction for unknown words. The scanner also classifies poems according to the predominant type of metrical foot found. We present a brief evaluation of the system using a gold standard corpus of humanscanned verse, on which a per-syllable accuracy of 86.78% is achieved.The program uses open-source components and is released under the GNU GPL license.

We describe a robot capable of composing and playing traditional Basque impromptu verses -bertsoak. The system, called Bertsobot, is able to construct improvised verses according to given constraints on rhyme and meter, and to perform it in public. Towards this end, several tools and applications have been developed and integrated in Bertsobot, including: speech-based communication system, text applications for verse generation, and robot behaviours to interact with the environment in a public performance. We describe the tools and processes behind our approach, present some early experimental results and illustrative verses, and finally, remark the conclusions and future steps.

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