Research Area
Year Published

430 Results

July 26, 2019

On the Distribution of Deep Clausal Embeddings: A Large Cross-linguistic Study

Association for Computational Linguistics (ACL)

We introduce here a collection of large, dependency-parsed written corpora in 17 languages, that allow us, for the first time, to capture clausal embedding through dependency graphs and assess their distribution.

By: Damián E. Blasi, Ryan Cotterell, Lawrence Wolf-Sonkin, Sabine Stoll, Balthasar Bickel, Marco Baroni

July 18, 2019

Tabula nearly rasa: Probing the linguistic knowledge of character-level neural language models trained on unsegmented text

Topology, Algebra and Categories in Logic (TACL)

Recurrent neural networks (RNNs) have reached striking performance in many natural language processing tasks. This has renewed interest in whether these generic sequence processing devices are inducing genuine linguistic knowledge. Nearly all current analytical studies, however, initialize the RNNs with a vocabulary of known words, and feed them tokenized input during training. We present a multi-lingual study of the linguistic knowledge encoded in RNNs trained as character-level language models, on input data with word boundaries removed.

By: Michael Hahn, Marco Baroni

July 18, 2019

Why Build an Assistant in Minecraft?

arXiv

In this document we describe a rationale for a research program aimed at building an open “assistant” in the game
Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue.

By: Arthur Szlam, Jonathan Gray, Kavya Srinet, Yacine Jernite, Armand Joulin, Gabriel Synnaeve, Douwe Kiela, Haonan Yu, Zhuoyuan Chen, Siddharth Goyal, Demi Guo, Danielle Rothermel, Larry Zitnick, Jason Weston

July 17, 2019

CraftAssist: A Framework for Dialogue-enabled Interactive Agents

This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions. The purpose of building such an assistant is to facilitate the study of agents that can complete tasks specified by dialogue, and eventually, to learn from dialogue interactions.

By: Jonathan Gray, Kavya Srinet, Yacine Jernite, Haonan Yu, Zhuoyuan Chen, Demi Guo, Siddharth Goyal, Larry Zitnick, Arthur Szlam

July 15, 2019

Searching for Communities: a Facebook Way

ACM SIGIR Conference on Research and Development in Information Retrieval

Giving people the power to build community is central to Facebook’s mission. Technically, searching for communities poses very different challenges compared to the standard IR problems.

By: Viet Ha-Thuc, Srinath Aaleti, Rongda Zhu, Nade Sritanyaratana, Corey Chen

July 12, 2019

VR Facial Animation via Multiview Image Translation

SIGGRAPH

In this work, we present a bidirectional system that can animate avatar heads of both users’ full likeness using consumer-friendly headset mounted cameras (HMC). There are two main challenges in doing this: unaccommodating camera views and the image-to-avatar domain gap. We address both challenges by leveraging constraints imposed by multiview geometry to establish precise image-to-avatar correspondence, which are then used to learn an end-to-end model for real-time tracking.

By: Shih-En Wei, Jason Saragih, Tomas Simon, Adam W. Harley, Stephen Lombardi, Michal Perdoch, Alexander Hypes, Dawei Wang, Hernan Badino, Yaser Sheikh

July 3, 2019

Linguistic generalization and compositionality in modern artificial neural networks

Philosophical Transactions of the Royal Society B

In the last decade, deep artificial neural networks have achieved astounding performance in many natural language processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely assumed that humans handle linguistic productivity by means of algebraic compositional rules: Are deep networks similarly compositional?

By: Marco Baroni

June 30, 2019

Perturbed-History Exploration in Stochastic Multi-Armed Bandits

International Joint Conference on Artificial Intelligence (IJCAI)

We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds O(t) i.i.d. pseudo-rewards to its history in round t and then pulls the arm with the highest average reward in its perturbed history.

By: Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier

June 30, 2019

Variational Training for Large-Scale Noisy-OR Bayesian Networks

Conference on Uncertainty in Artificial Intelligence (UAI)

We propose a stochastic variational inference algorithm for training large-scale Bayesian networks, where noisy-OR conditional distributions are used to capture higher-order relationships. One application is to the learning of hierarchical topic models for text data.

By: Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth

June 21, 2019

Randomized Value Functions via Multiplicative Normalizing Flows

Conference on Uncertainty in Artificial Intelligence (UAI)

In this work, we leverage recent advances in variational Bayesian neural networks and combine these with traditional Deep Q-Networks (DQN) and Deep Deterministic Policy Gradient (DDPG) to achieve randomized value functions for high-dimensional domains.

By: Ahmed Touati, Harsh Satija, Joshua Romoff, Joelle Pineau, Pascal Vincent