Humans generally use natural language to communicate task requirements to each other. Ideally, natural language should also be usable for communicating goals to autonomous machines (e.g., robots) to minimize friction in task specification. However, understanding and mapping natural language goals to sequences of states and actions is challenging. Specifically, existing work along these lines has encountered difficulty in generalizing learned policies to new natural language goals and environments. In this paper, we propose a novel adversarial inverse reinforcement learning algorithm to learn a language-conditioned policy and reward function. To improve generalization of the learned policy and reward function, we use a variational goal generator to relabel trajectories and sample diverse goals during training. Our algorithm outperforms multiple baselines by a large margin on a vision-based natural language instruction following dataset (Room-2-Room), demonstrating a promising advance in enabling the use of natural language instructions in specifying agent goals.

Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with selfcontained, summary-centric questions and length-constrained, article-summarizing answers.We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length.This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions.We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation.Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.1

In this paper we present a novel data structure for sparse vectors based on Cuckoo hashing. It is highly memory efficient and allows for random access at near dense vector level rates. This allows us to solve sparse l1 programming problems exactly and without preprocessing at a cost that is identical to dense linear algebra both in terms of memory and speed. Our approach provides a feasible alternative to the hash kernel and it excels whenever exact solutions are required, such as for feature selection.

Topic models have played a pivotal role in analyzing large collections of complex data. Besides discovering latent semantics, supervised topic models (STMs) can make predictions on unseen test data. By marrying with advanced learning techniques, the predictive strengths of STMs have been dramatically enhanced, such as max-margin supervised topic models, state-of-the-art methods that integrate max-margin learning with topic models. Though powerful, max-margin STMs have a hard non-smooth learning problem. Existing algorithms rely on solving multiple latent SVM subproblems in an EM-type procedure, which can be too slow to be applicable to large-scale categorization tasks.