Final project for CS224N: Natural Language Processing with Deep Learning.
Advances in deep learning and neural networks in recent years have led to impressive advancements in text generation. However, text generation by neural networks is still often incomprehensible to a human reader beyond a single sentence. This problem is especially noticeable in film dialogue scripts. In a script, each line of dialogue is made by a particular character, with the next line of dialogue often being a response to that speaker from another speaker. Our goal in this paper is to improve the flow of exchange between characters in the script dialogue, namely, when exactly characters choose to speak and what they say in response to each other. We propose a natural language generation model using a neural machine translation context. Words spoken to a certain character in a script are treated as source sentences, and their responses are target sentences. Using this unique model, we are able to significantly improve on coherence and humor when compared to Recurrent Neural Network language models.
https://github.com/samjkwong/cs224n-final/blob/master/paper.pdf
https://github.com/samjkwong/cs224n-final/blob/master/poster.pdf