We investigate the task of modeling open-domain, multi-turn, unstructured, multi-participant, conversational dialogue. We specifically study the effect of incorporating different elements of the conversation. Unlike previous efforts, which focused on modeling messages and responses, we extend the modeling to long context and participant's history. Our system does not rely on handwritten rules or engineered features; instead, we train deep neural networks on a large conversational dataset. In particular, we exploit the structure of Reddit comments and posts to extract 2.1 billion messages and 133 million conversations. We evaluate our models on the task of predicting the next response in a conversation, and we find that modeling both context and participants improves prediction accuracy.
Personalizing dialogue systems requires sufficient information from each user and a sufficient user population to define the space. Writing styles quantified by word length, verb strength, polarity, and distribution of dialogue acts have been used to model users @cite_15 . Other efforts focused on building a user profile based on demographics, such as gender, income, age, and marital status @cite_18 . Because Reddit users are pseudo-anonymous, we differ from these approaches by learning the relevant features to model the users' dialogue behavior through embedding each user into a distributed representation.
@cite_15 @cite_18
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Domestic abuse affects people of every race, class, age, and nation. There is significant research on the prevalence and effects of domestic abuse; however, such research typically involves population-based surveys that have high financial costs. This work provides a qualitative analysis of domestic abuse using data collected from the social and news-aggregation website reddit.com. We develop classifiers to detect submissions discussing domestic abuse, achieving accuracies of up to 92 , a substantial error reduction over its baseline. Analysis of the top features used in detecting abuse discourse provides insight into the dynamics of abusive relationships. This papers presents a context-aware NLP approach to automatically detect noteworthy information in spontaneous mobile phone conversations. The proposed method uses a supervised modeling strategy which considers both features from the content of the conversation as well as contextual information from the call. We empirically analyze the predictive performance of features of different nature on a corpus of mobile phone conversations. The results of this study reveal that the context of the conversation plays a crucial role on boosting the predictive performance of the model.