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Identifying (dis)agreement in dialogueJuly 1st, 2019
This paper replicates the model described in ”The role of polarity in inferring acceptance and rejection in dialogue” by Schl¨oder and Fern´andez (2014) to explore the use of logistic regression, as well as reduce the amount of features needed to produce a similar F1 score on (dis)agreement detection in spoken dialogue. To achieve similar results as the original Bernoulli naive Bayes model, downsampling acceptances in an imbalanced data set like the AMI meeting corpus proves to be crucial for the effectiveness of logistic regression. This paper reduces the set of features from the original model by Schl¨oder and Fern´andez (2014) to positive-negative polarity, negative-positive parallelism, checking for ”But” and ”Yeah but ” in the response, acceptance cues and response length while barely losing performance measured in F1 score (60.77 compared to 60.86). When applying this new model to the ABCD corpus, it still performs well when compared to the work by Rosenthal and McKeown (2015), a
