Daniel Spokoyny


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An Empirical Investigation of Contextualized Number Prediction
Taylor Berg-Kirkpatrick | Daniel Spokoyny
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We conduct a large scale empirical investigation of contextualized number prediction in running text. Specifically, we consider two tasks: (1)masked number prediction– predict-ing a missing numerical value within a sentence, and (2)numerical anomaly detection–detecting an errorful numeric value within a sentence. We experiment with novel combinations of contextual encoders and output distributions over the real number line. Specifically, we introduce a suite of output distribution parameterizations that incorporate latent variables to add expressivity and better fit the natural distribution of numeric values in running text, and combine them with both recur-rent and transformer-based encoder architectures. We evaluate these models on two numeric datasets in the financial and scientific domain. Our findings show that output distributions that incorporate discrete latent variables and allow for multiple modes outperform simple flow-based counterparts on all datasets, yielding more accurate numerical pre-diction and anomaly detection. We also show that our models effectively utilize textual con-text and benefit from general-purpose unsupervised pretraining.


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Explicit Causal Connections between the Acquisition of Linguistic Tiers: Evidence from Dynamical Systems Modeling
Daniel Spokoyny | Jeremy Irvin | Fermin Moscoso del Prado Martin
Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning