An Empirical Investigation of Contextualized Number Prediction
This work addresses contextualized number prediction for applications in domains like finance and science, representing an incremental improvement through novel combinations of existing methods.
The paper tackled the problem of predicting and detecting numerical values in text by experimenting with novel output distributions that incorporate latent variables, finding that these multi-modal distributions outperformed simpler flow-based methods on financial and scientific datasets, yielding more accurate predictions and anomaly detection.
We conduct a large scale empirical investigation of contextualized number prediction in running text. Specifically, we consider two tasks: (1)masked number prediction-predicting 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 recurrent 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 prediction and anomaly detection. We also show that our models effectively utilize textual con-text and benefit from general-purpose unsupervised pretraining.