Continual Learning for Neural Semantic Parsing

A semantic parsing model is crucial to natural language processing applications such as goal-oriented dialogue systems. Such models can have hundreds of classes with a highly non-uniform distribution. In this work, we show how to efficiently (in terms of computational budget) improve model performance given a new portion of labeled data for a specific low-resource class or a set of classes. We demonstrate that a simple approach with a specific fine-tuning procedure for the old model can reduce the computational costs by ~90% compared to the training of a new model. The resulting performance is on-par with a model trained from scratch on a full dataset. We showcase the efficacy of our approach on two popular semantic parsing datasets, Facebook TOP, and SNIPS.

Modeling Scalability of Distributed Machine Learning

Present day machine learning is computationally intensive and processes large amounts of data. It is implemented in a distributed fashion in order to address these scalability issues. The work is parallelized across a number of computing nodes. It is usually hard to estimate in advance how many nodes to use for a particular workload. We propose a simple framework for estimating the scalability of distributed machine learning algorithms. We measure the scalability by means of the speedup an algorithm achieves with more nodes. We propose time complexity models for gradient descent and graphical model inference. We validate our models with experiments on deep learning training and belief propagation. This framework was used to study the scalability of machine learning algorithms in Apache Spark.