Recent advances in tabular deep learning have demonstrated the importance of embeddings for numerical features, where scalar values are mapped to high-dimensional spaces before being processed by the main model. Here, we propose an embedding method using the hyperbolic tangent (tanh) activation function that allows neural networks to achieve better accuracy on tabular data via an inductive bias similar to that of decision trees. To make training with the new embedding method reliable and efficient, we additionally propose a principled initialization method. Experiments demonstrate that the new approach improves upon or matches accuracy results from previously proposed embedding methods across multiple tabular datasets and model architectures.

In task-oriented dialog, agents need to generate both fluent natural language responses and correct external actions like database queries and updates. Our paper makes the first attempt at evaluating state of the art models on a large real world task with human users. We show that methods that achieve state of the art performance on synthetic datasets, perform poorly in real world dialog tasks. We propose a hybrid model, where nearest neighbor is used to generate fluent responses and Seq2Seq type models ensure dialogue coherency and generate accurate external actions. The hybrid model on the customer support data achieves a 78% relative improvement in fluency, and a 200% improvement in accuracy of external calls.