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Adversarial Training for Textual Entailment with Knowledge-Guided Examples
Tushar Khot, Ashish Sabharwal and Dongyeop KangACL • 2018 We consider the problem of learning textual entailment models with limited supervision (5K-10K training examples), and present two complementary approaches for it. First, we propose knowledge-guided adversarial example generators for incorporating large…AllenNLP: A Deep Semantic Natural Language Processing Platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson Liu, Matthew Peters, Michael Schmitz, Luke ZettlemoyerACL • NLP OSS Workshop • 2018 This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily. It is built on top of…Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, Aniruddha KembhaviCVPR • 2018 A number of studies have found that today’s Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. To encourage development of models geared towards the latter, we…ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
Sachin Mehta, Mohammad Rastegari, Anat Caspi, Linda Shapiro, and Hannaneh HajishirziECCV • 2018 We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms…Event2Mind: Commonsense Inference on Events, Intents, and Reactions
Maarten Sap, Hannah Rashkin, Emily Allaway, Noah A. Smith and Yejin ChoiACL • 2018 We investigate a new commonsense inference task: given an event described in a short free-form text (“X drinks coffee in the morning”), a system reasons about the likely intents (“X wants to stay awake”) and reactions (“X feels alert”) of the event’s…Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples
Vidur Joshi, Matthew Peters, and Mark HopkinsACL • 2018 We revisit domain adaptation for parsers in the neural era. First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is syntactically similar to the source domain. As evidence, we train…Imagine This! Scripts to Compositions to Videos
Tanmay Gupta, Dustin Schwenk, Ali Farhadi, Derek Hoiem, and Aniruddha KembhaviECCV • 2018 Imagining a scene described in natural language with realistic layout and appearance of entities is the ultimate test of spatial, visual, and semantic world knowledge. Towards this goal, we present the Composition, Retrieval and Fusion Network (Craft), a…IQA: Visual Question Answering in Interactive Environments
Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, Ali FarhadiCVPR • 2018 We introduce Interactive Question Answering (IQA), the task of answering questions that require an autonomous agent to interact with a dynamic visual environment. IQA presents the agent with a scene and a question, like: “Are there any apples in the fridge…Learning to Write with Cooperative Discriminators
Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub and Yejin ChoiACL • 2018 Despite their local fluency, long-form text generated from RNNs is often generic, repetitive, and even self-contradictory. We propose a unified learning framework that collectively addresses all the above issues by composing a committee of discriminators that…LSTMs Exploit Linguistic Attributes of Data
Nelson F. Liu, Omer Levy, Roy Schwartz, Chenhao Tan, Noah A. SmithACL • RepL4NLP Workshop • 2018 While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the properties of natural language data affect an LSTM's ability to learn a…