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60 Results

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
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
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

A Dataset for Telling the Stories of Social Media Videos

Empirical Methods in Natural Language Processing (EMNLP)

Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication.

By: Spandana Gella, Mike Lewis, Marcus Rohrbach