Yonatan Bisk

Publications
Influence

SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference

Rowan Zellers, Yonatan Bisk, Roy Schwartz, Yejin Choi
Computer Science
EMNLP
16 August 2018

We introduce 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

Defending Against Neural Fake News

Rowan Zellers, Ari Holtzman, +4 authors Yejin Choi
Computer Science
NeurIPS
29 May 2019

We present a new generative model for controllable text generation called Grover. Expand

From Recognition to Cognition: Visual Commonsense Reasoning

Rowan Zellers, Yonatan Bisk, Ali Farhadi, Yejin Choi
Computer Science
IEEE/CVF Conference on Computer Vision and…
27 November 2018

We present a new reasoning engine, Recognition to Cognition Networks (R2C), that models the necessary layered inferences for grounding, contextualization, and reasoning. Expand

Synthetic and Natural Noise Both Break Neural Machine Translation

Yonatan Belinkov, Yonatan Bisk
Computer Science
ICLR
6 November 2017

Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Expand

Supertagging With LSTMs

Ashish Vaswani, Yonatan Bisk, Kenji Sagae, Ryan Musa
Computer Science
HLT-NAACL
1 June 2016

We introduce a new bi–LSTM model that achieves state-of-the-art performance on CCG supertagging and parsing, and a detailed analysis of the quality of various LSTM architectures, forward, backward, and bi-directional. Expand

HellaSwag: Can a Machine Really Finish Your Sentence?

Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, Yejin Choi
Computer Science
ACL
1 May 2019

We show that commonsense inference still proves difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset. Expand

Natural Language Communication with Robots

Yonatan Bisk, Deniz Yuret, D. Marcu
Computer Science
HLT-NAACL
1 June 2016

We propose a framework for devising empirically testable algorithms for bridging the communication gap between humans and robots, in which humans give instructions to robots using unrestricted natural language commands. Expand

Tactical Rewind: Self-Correction via Backtracking in Vision-And-Language Navigation

Liyiming Ke, Xiujun Li, +6 authors S. Srinivasa
Computer Science
IEEE/CVF Conference on Computer Vision and…
6 March 2019

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) Vision-and-Language navigation challenge. Expand

Unsupervised Neural Hidden Markov Models

Ke M. Tran, Yonatan Bisk, Ashish Vaswani, D. Marcu, Kevin Knight
Computer Science
SPNLP@EMNLP
28 September 2016

In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model using Expectation-Maximization and Variational Inference. Expand

ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

Mohit Shridhar, Jesse Thomason, +5 authors D. Fox
Computer Science
IEEE/CVF Conference on Computer Vision and…
3 December 2019

We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. Expand

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