12 research outputs found
Neural poetry: learning to generate poems using syllables
No full textMotivated by the recent progresses on machine learning-based models that learn artistic styles, in this paper we focus on the problem of poem generation. This is a challenging task in which the machine has to capture the linguistic features that strongly characterize a certain poet, as well as the semantics of the poet’s production, that are influenced by his personal experiences and by his literary background. Since poetry is constructed using syllables, that regulate the form and structure of poems, we propose a syllable-based neural language model, and we describe a poem generation mechanism that is designed around the poet style, automatically selecting the most representative generations. The poetic work of a target author is usually not enough to successfully train modern deep neural networks, so we propose a multi-stage procedure that exploits non-poetic works of the same author, and also other publicly available huge corpora to learn syntax and grammar of the target language. We focus on the Italian poet Dante Alighieri, widely famous for his Divine Comedy. A quantitative and qualitative experimental analysis of the generated tercets is reported, where we included expert judges with strong background in humanistic studies. The generated tercets are frequently considered to be real by a generic population of judges, with relative difference of 56.25% with respect to the ones really authored by Dante, and expert judges perceived Dante’s style and rhymes in the generated text
Generate and Revise: Reinforcement Learning in Neural Poetry
No full textWriters, poets, singers usually do not create their compositions in just one breath. Text is revisited, adjusted, modified, rephrased, even multiple times, in order to better convey meanings, emotions and feelings that the author wants to express. Amongst the noble written arts, Poetry is probably the one that needs to be elaborated the most, since the composition has to formally respect predefined meter and rhyming schemes. In this paper, we propose a framework to generate poems that are repeatedly revisited and corrected, as humans do, in order to improve their overall quality. We frame the problem of revising poems in the context of Reinforcement Learning and, in particular, using Proximal Policy Optimization. Our model generates poems from scratch and it learns to progressively adjust the generated text in order to match a target criterion. We evaluate this approach in the case of matching a rhyming scheme, without having any information on which words are responsible of creating rhymes and on how to coherently alter the poem words. The proposed framework is general and, with an appropriate reward shaping, it can be applied to other text generation problems
Radar sensitivity and resolution in presence of range sidelobe reducing networks designed using linear programming
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MARSIS data inversion approach: Preliminary results
No full textAn approach to the inversion of the data available from the MARSIS (Mars Advanced Radar for Subsurface and Ionosphere Sounding) instrument on Mars Express is described. The data inversion gives an estimation of the materials composing the different detected interfaces, including the impurity (inclusion) of the first layer, if any, and its percentage, by the evaluation of the values of the permittivity that would generate the observed radio echoes. The data inversion method is based on the analysis of the surface to subsurface power ratio and the relative time delay as measured by MARSIS. The constraints, due to the known geological history of the surface, the local temperature and the thermal condition of the observed zones and the results of other instruments on Mars Express and other missions to Mars, have to be considered to improve the validity of the utilized models and the obtained results that are given in parametric way
