Research Area
Year Published

65 Results

December 14, 2018

PyText: A seamless path from NLP research to production

By: Ahmed Aly, Kushal Lakhotia, Shicong Zhao, Mrinal Mohit, Barlas Oguz, Abhinav Arora, Sonal Gupta, Christopher Dewan, Stef Nelson-Lindall, Rushin Shah

December 4, 2018

Improving Semantic Parsing for Task Oriented Dialog

Conversational AI Workshop at NeurIPS 2018

Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results. In this paper, we present three different improvements to the model: contextualized embeddings, ensembling, and pairwise re-ranking based on a language model.

By: Arash Einolghozati, Panupong Pasupat, Sonal Gupta, Rushin Shah, Mrinal Mohit, Mike Lewis, Luke Zettlemoyer

December 3, 2018

Explore-Exploit: A Framework for Interactive and Online Learning

Systems for Machine Learning Workshop at NeurIPS 2018

We present Explore-Exploit: a framework designed to collect and utilize user feedback in an interactive and online setting that minimizes regressions in end-user experience. This framework provides a suite of online learning operators for various tasks such as personalization ranking, candidate selection and active learning.

By: Honglei Liu, Anuj Kumar, Wenhai Yang, Benoit Dumoulin

November 2, 2018

Do explanations make VQA models more predictable to a human?

Empirical Methods in Natural Language Processing (EMNLP)

A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable ‘explanations’ of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model – its responses as well as failures – more predictable to a human.

By: Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, Devi Parikh

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 2, 2018

Dynamic Meta-Embeddings for Improved Sentence Representations

Empirical Methods in Natural Language Processing (EMNLP)

While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves.

By: Douwe Kiela, Changhan Wang, Kyunghyun Cho

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

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

Neural Compositional Denotational Semantics for Question Answering

Conference on Empirical Methods in Natural Language Processing (EMNLP)

Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics.

By: Nitish Gupta, Mike Lewis