Conversational Flow in Oxford-style Debates

Abstract

Public debates are a common platform for presenting and juxtaposing diverging views on important issues. In this work we propose a methodology for tracking how ideas flow between participants throughout a debate. We use this approach in a case study of Oxfordstyle debates-a competitive format where the winner is determined by audience votes-and show how the outcome of a debate depends on aspects of conversational flow. In particular, we find that winners tend to make better use of a debate's interactive component than losers, by actively pursuing their opponents' points rather than promoting their own ideas over the course of the conversation.

Towards Debate Automation: a Recurrent Model for Predicting Debate Winners

“…Att means the model has the attention mechanism from Section 3.2.1; Reg means the model uses the optimization objective from Equation 9 (all other models use the optimization objective from Equation 4); Drpt means the model uses dropout (a popular regularization technique for neural networks (Srivastava et al, 2014)) of 0.5. We compare our results against the best models from Zhang et al (2016). Each model uses a Logistic Regression (LR) classifier, and distinguishes itself by the features it uses.…”

“…The model uses an attention mechanism that creates a weighted sum over all hidden states. In order to achieve state-of-the-art results on a corpus of debate transcripts (Zhang et al, 2016), we regularize the RNN model by propagating errors based on implicit audience reaction. Our results show that this regularization technique is critical for obtaining a state-of-the-art result.…”

“…Our work follows that of Zhang et al (2016), and uses talking point-based features, specifically a 'bag of talking points'. Talking points for each debate are identified using a term frequency inverse document frequency (tfidf) metric applied to text tokens.…”

“…Our experiments are conducted on the IQ2 dataset (Zhang et al, 2016). We use LOO evaluation, resulting in a training set of 107 examples.…”

“…Logistic regression, on the other hand, would not allow us to model the sequence explicitly. Secondly, our model allows us to take raw features as input, without having to compute summary statistics necessary for the fea-tures used in the model of Zhang et al (2016). Finally, since our end goal is debate automation, an RNN is a natural choice for debate turn generation.…”