Research Area
Year Published

598 Results

November 2, 2018

Jump to better conclusions: SCAN both left and right

Empirical Methods in Natural Language Processing (EMNLP)

Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability to extract systematic rules. Here, we take a closer look at SCAN and show that it does not always capture the kind of generalization that it was designed for.

By: Joost Bastings, Marco Baroni, Jason Weston, Kyunghyun Cho, Douwe Kiela
November 1, 2018

Social media governance: Can companies motivate voluntary rule following behavior among their users

Social Media Governance Workshop

The question of how to effectively enforce rules on social media mirrors the general question of how to enforce laws in society. One model for both governments and private companies is incapacitation—preventing people from taking particular actions.

By: Tom Tyler, Matt Katsaros, Tracey Meares, Sudhir Venkatesh
November 1, 2018

How agents see things: On visual representations in an emergent language game

Empirical Methods in Natural Language Processing (EMNLP)

There is growing interest in the language developed by agents interacting in emergent-communication settings. Earlier studies have focused on the agents’ symbol usage, rather than on their representation of visual input. In this paper, we consider the referential games of Lazaridou et al. (2017), and investigate the representations the agents develop during their evolving interaction.

By: Diane Bouchacourt, Marco Baroni
November 1, 2018

Non-Adversarial Unsupervised Word Translation

Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we make the observation that two sufficiently similar distributions can be aligned correctly with iterative matching methods.

By: Yedid Hoshen, Lior Wolf
November 1, 2018

Horizon: Facebook’s Open Source Applied Reinforcement Learning Platform

ArXiv

In this paper we present Horizon, Facebook’s open source applied reinforcement learning (RL) platform. Horizon is an end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator.

By: Jason Gauci, Edoardo Conti, Yitao Liang, Kittipat Virochsiri, Yuchen He, Zachary Kaden, Vivek Narayanan, Xiaohui Ye
October 31, 2018

Semantic Parsing for Task Oriented Dialog using Hierarchical Representations

Conference on Empirical Methods in Natural Language Processing (EMNLP)

Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots. Previous work on…

By: Sonal Gupta, Rushin Shah, Mrinal Mohit, Anuj Kumar, Mike Lewis
October 31, 2018

Understanding Back-Translation at Scale

Empirical Methods in Natural Language Processing (EMNLP)

An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number of methods to generate synthetic source sentences.

By: Sergey Edunov, Myle Ott, Michael Auli, David Grangier
October 31, 2018

Extending Neural Generative Conversational Model using External Knowledge Sources

Empirical Methods in Natural Language Processing (EMNLP)

The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often lack coherence and are content poor. This work proposes an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models.

By: Prasanna Parthasarathi, Joelle Pineau
October 31, 2018

Training Millions of Personalized Dialogue Agents

Empirical Methods in Natural Language Processing (EMNLP)

In this paper we introduce a new dataset providing 5 million personas and 700 million persona-based dialogues.

By: Pierre-Emmanuel Mazaré, Samuel Humeau, Martin Raison, Antoine Bordes
October 31, 2018

Auto-Encoding Dictionary Definitions into Consistent Word Embeddings

Empirical Methods in Natural Language Processing (EMNLP)

Monolingual dictionaries are widespread and semantically rich resources. This paper presents a simple model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them.

By: Tom Bosc, Pascal Vincent