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SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference
This paper introduces the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning, and proposes Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. Expand
From Recognition to Cognition: Visual Commonsense Reasoning
To move towards cognition-level understanding, a new reasoning engine is presented, Recognition to Cognition Networks (R2C), that models the necessary layered inferences for grounding, contextualization, and reasoning. Expand
Defending Against Neural Fake News
A model for controllable text generation called Grover, found that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data, and the best defense against Grover turns out to be Grover itself, with 92% accuracy. Expand
Synthetic and Natural Noise Both Break Neural Machine Translation
It is found that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise, including structure-invariant word representations and robust training on noisy texts. Expand
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
It is shown that a baseline model based on recent embodied vision-and-language tasks performs poorly on ALFRED, suggesting that there is significant room for developing innovative grounded visual language understanding models with this benchmark. Expand
HellaSwag: Can a Machine Really Finish Your Sentence?
The construction of HellaSwag, a new challenge dataset, and its resulting difficulty, sheds light on the inner workings of deep pretrained models, and suggests a new path forward for NLP research, in which benchmarks co-evolve with the evolving state-of-the-art in an adversarial way, so as to present ever-harder challenges. Expand
PIQA: Reasoning about Physical Commonsense in Natural Language
The task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA are introduced and analysis about the dimensions of knowledge that existing models lack are provided, which offers significant opportunities for future research. Expand
Tactical Rewind: Self-Correction via Backtracking in Vision-And-Language Navigation
We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the 2018 Room-to-Room (R2R)Expand
Supertagging With LSTMs
This paper presents new state-of-the-art performance on CCG supertagging and parsing and demonstrates that while feed-forward architectures can compete with bidirectional LSTMs on POS tagging, models that encode the complete sentence are necessary for the long range syntactic information encoded in supertags. Expand
Natural Language Communication with Robots
It is shown how one can collect meaningful training data and the proposed three neural architectures for interpreting contextually grounded natural language commands allow us to correctly understand/ground the blocks that the robot should move when instructed by a human who uses unrestricted language. Expand