6 accepted papers
In this paper, we model aspects of communication beyond the words that are said. Specifically, we aim to detect interruptions and active listening events, which are important elements in any dialogue. We build a dataset with fine-grained annotations for each category and train multimodal models that…
We introduce an adaptive tree search algorithm, which is a deterministic variant of Monte Carlo tree search, that can find high-scoring outputs under translation models that make no assumptions about the form or structure of the search objective. This algorithm enables the exploration of new kinds o…
We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i.e., a sentence). Our formulation provides an alternative perspective that unifies classical word embedding models…
We present a new theoretical perspective of data noising in recurrent neural network language models (Xie et al., 2017). We show that each variant of data noising is an instance of Bayesian recurrent neural networks with a particular variational distribution (i.e., a mixture of Gaussians whose weig…
We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models. Our experiments on the Penn Treebank and Wikitext-2 datasets show that stack-based memory architectures consistently achieve the best performance in terms of held out perple…
We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences. In contrast with prior work on tree-structured models, in which the trees are either provided as input or predicted using supervision from explicit treebank annotations…