Arabic Poem Generation Incorporating Deep Learning and Phonetic CNNsubword Embedding Models

Abstract

Arabic poetry generation is a very challenging task since the linguistic structure of the Arabic language is considered a severe challenge for many researchers and developers in the Natural Language Processing (NLP) field. In this paper, we propose a poetry generation model with extended phonetic and semantic embeddings (Phonetic CNNsubword embeddings). We show that Phonetic CNNsubword embeddings have an effective contribution to the overall model performance compared to FastTextsubword embeddings. Our poetry generation model consists of a two-stage approach: (1.) generating the first verse which explicitly incorporates the theme related phrase, (2.) other verses generation with the proposed Hierarchy-Attention Sequence-to-Sequence model (HAS2S), which adequately capture word, phrase, and verse information between contexts. A comprehensive human evaluation confirms that the poems generated by our model outperform the base models in criteria such as Meaning, Coherence, Fluency, and Poeticness. Extensive quantitative experiments using Bi-Lingual Evaluation Understudy (BLEU) scores also demonstrate significant improvements over strong baselines.