Arthur Spirling


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Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop
Rujun Han | Michael Gill | Arthur Spirling | Kyunghyun Cho
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Conventional word embedding models do not leverage information from document meta-data, and they do not model uncertainty. We address these concerns with a model that incorporates document covariates to estimate conditional word embedding distributions. Our model allows for (a) hypothesis tests about the meanings of terms, (b) assessments as to whether a word is near or far from another conditioned on different covariate values, and (c) assessments as to whether estimated differences are statistically significant.


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Asking too much? The rhetorical role of questions in political discourse
Justine Zhang | Arthur Spirling | Cristian Danescu-Niculescu-Mizil
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Questions play a prominent role in social interactions, performing rhetorical functions that go beyond that of simple informational exchange. The surface form of a question can signal the intention and background of the person asking it, as well as the nature of their relation with the interlocutor. While the informational nature of questions has been extensively examined in the context of question-answering applications, their rhetorical aspects have been largely understudied. In this work we introduce an unsupervised methodology for extracting surface motifs that recur in questions, and for grouping them according to their latent rhetorical role. By applying this framework to the setting of question sessions in the UK parliament, we show that the resulting typology encodes key aspects of the political discourse—such as the bifurcation in questioning behavior between government and opposition parties—and reveals new insights into the effects of a legislator’s tenure and political career ambitions.