what is the size of the dataset?
2771 speeches containing 50871 sentences
In this report we present a system that can generate political speeches for a desired political party. Furthermore, the system allows to specify whether a speech should hold a supportive or opposing opinion. The system relies on a combination of several state-of-the-art NLP methods which are discussed in this report. These include n-grams, Justeson&Katz POS tag filter, recurrent neural networks, and latent Dirichlet allocation. Sequences of words are generated based on probabilities obtained from two underlying models: A language model takes care of the grammatical correctness while a topic model aims for textual consistency. Both models were trained on the Convote dataset which contains transcripts from US congressional floor debates. Furthermore, we present a manual and an automated approach to evaluate the quality of generated speeches. In an experimental evaluation generated speeches have shown very high quality in terms of grammatical correctness and sentence transitions.
Many political speeches show the same structures and same characteristics regardless of the actual topic. Some phrases and arguments appear again and again and indicate a certain political affiliation or opinion. We want to use these remarkable patterns to train a system that generates new speeches. Since there are major differences between the political parties we want the system to consider the political affiliation and the opinion of the intended speaker. The goal is to generate speeches where no one can tell the difference to hand-written speeches. In this report we first discuss related works which deal with similar or related methods. Then we describe and analyze the dataset we use. Next, we present the methods we used to implement our system. We also describe investigated methods that were not used in the final implementation. Then we describe a performed experiment and how we evaluated the results. Finally, we conclude our work and give an outlook. The appendix of this report contains the generated speeches from the experiment.