Modelling persuasion strategies as predictors of task outcome has several real-world applications and has received considerable attention from the computational linguistics community. However, previous research has failed to account for the resisting strategies employed by an individual to foil such persuasion attempts. Grounded in prior literature in cognitive and social psychology, we propose a generalised framework for identifying resisting strategies in persuasive conversations. We instantiate our framework on two distinct datasets comprising persuasion and negotiation conversations. We also leverage a hierarchical sequence-labelling neural architecture to infer the aforementioned resisting strategies automatically. Our experiments reveal the asymmetry of power roles in non-collaborative goal-directed conversations and the benefits accrued from incorporating resisting strategies on the final conversation outcome. We also investigate the role of different resisting strategies on the conversation outcome and glean insights that corroborate with past findings. We also make the code and the dataset of this work publicly available at https://github.com/americast/ resper.
Online learning communities provide open workspaces allowing learners to share information, exchange ideas, address problems and discuss on specific themes. But with the continuously increasing artifacts in online communities, learners feel it difficult to quickly and easily gain an insight into a certain theme. To facilitate and support learners have a better understanding of the communication focus, this paper presents an approach to discover the hot topics and patterns of topics evolutions in online learning communities. Firstly, hot terms are extracted based on three features: the frequency of the terms used in the document collection; the location of the terms within a document; the breadth of terms distribution in the document collection. Then a term association network is constructed by computing the terms co-occurrence and distance between them. Finally, an algorithm is proposed to select the kernel term and its associated terms as term clusters to represent the hot topics with multi-facets expression. Two case studies on real datasets are conducted to demonstrate the effectiveness and usefulness of term cluster in helping users better understand hot topics in online learning communities. Potential applications in learning scenarios are also discussed.
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