Natural Language Processing
Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks. …
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive …
In this work, we investigate the performance of untrained randomly initialized encoders in a general class of sequence to sequence …
This paper explores the impact of warnings, audio feedback, and gender on human-robot trust in the context of autonomous driving and …
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and …
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational …
Recent progress in deep generative models has been fueled by two paradigms – au- toregressive and adversarial models. We propose a …
This article proposes Adversarially-Trained Normalized Noisy-Feature Auto-Encoder (ATNNFAE) for byte-level text generation. An ATNNFAE …
In Reinforcement Learning (RL), an agent is guided by the rewards it receives from the reward function. Unfortunately, it may take many …
We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of …