In this paper , we propose a joint architecture that captures language , rhyme and meter for sonnet modelling . We assess the quality of generated poems using crowd and expert judgements . The stress and rhyme models perform very well , as generated poems are largely indistinguishable from human-written poems . Expert evaluation , however , reveals that a vanilla language model captures meter implicitly , and that machine-generated poems still underperform in terms of readability and emotion . Our research shows the importance expert evaluation for poetry generation , and that future research should look beyond rhyme / meter and focus on poetic language . With the recent surge of interest in deep learning , one question that is being asked across a number of fronts is : can deep learning techniques be harnessed for creative purposes ? Creative applications where such research exists include the composition of music ( Humphrey et al . , 2013 ; Sturm et al . , 2016 ; Choi et al . , 2016 ) , the design of sculptures ( Lehman et al . , 2016 ) , and automatic choreography ( Crnkovic-Friis and Crnkovic-Friis , 2016 ) . In this paper , we focus on a creative textual task : automatic poetry composition . A distinguishing feature of poetry is its aesthetic forms , e.g. rhyme and rhythm / meter . 1 In this work , we treat the task of poem generation as a constrained language modelling task , such that lines of a given poem rhyme , and each line follows a canonical meter and has a fixed number 1 Noting that there are many notable divergences from this in the work of particular poets ( e.g. Walt Whitman ) and poetry types ( such as free verse or haiku ) . Shall I compare thee to a summer 's day ? Thou art more lovely and more temperate : Rough winds do shake the darling buds of May , And summer 's lease hath all too short a date : of stresses . Specifically , we focus on sonnets and generate quatrains in iambic pentameter ( e.g. see Figure 1 ) , based on an unsupervised model of language , rhyme and meter trained on a novel corpus of sonnets . Our findings are as follows : • our proposed stress and rhyme models work very well , generating sonnet quatrains with stress and rhyme patterns that are indistinguishable from human-written poems and rated highly by an expert ; • a vanilla language model trained over our sonnet corpus , surprisingly , captures meter implicitly at human-level performance ; • while crowd workers rate the poems generated by our best model as nearly indistinguishable from published poems by humans , an expert annotator found the machine-generated poems to lack readability and emotion , and our best model to be only comparable to a vanilla language model on these dimensions ; • most work on poetry generation focuses on meter ( Greene et al . , 2010 ; Ghazvininejad et al . , 2016 ; Hopkins and Kiela , 2017 ) ; our results suggest that future research should look beyond meter and focus on improving readability . In this , we develop a new annotation framework for the evaluation of machine-generated poems , and release both a novel data of sonnets and the full source code associated with this research.2 We propose a joint model of language , meter and rhyme that captures language and form for modelling sonnets . We provide quantitative analyses for each component , and assess the quality of generated poems using judgements from crowdworkers and a literature expert . Our research reveals that vanilla LSTM language model captures meter implicitly , and our proposed rhyme model performs exceptionally well . Machine-generated generated poems , however , still underperform in terms of readability and emotion .

Challenge: The effectiveness of deep learning techniques for creative purposes such as automatic poetry composition which has its aesthetic forms remains unknown.
Approach: They propose a joint architecture that captures language, rhyme and meter for sonnet modelling to generate quatrains coupled with a corpus to train.
Outcome: Evaluation with the crowd and expert judgement reveals that while models perform indistinguishable from human-written poems on stress and rhyme, they lack readability and emotion.