most citedLeveraging Language for Accelerated Learning of Tool Manipulation

6 citations · 11 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20222 cited

Can Rationalization Improve Robustness?

Howard Chen, Jacqueline He, Karthik Narasimhan +1

A growing line of work has investigated the development of neural NLP models that can produce rationales--subsets of input that can explain their model predictions. In this paper,…

cs.CL20227 cited

Linking Emergent and Natural Languages via Corpus Transfer

Shunyu Yao, Mo Yu, Yang Zhang +3

The study of language emergence aims to understand how human languages are shaped by perceptual grounding and communicative intent. Computational approaches to emergent communicati…

cs.CL20222 cited

CARETS: A Consistency And Robustness Evaluative Test Suite for VQA

Carlos E. Jimenez, Olga Russakovsky, Karthik Narasimhan

We introduce CARETS, a systematic test suite to measure consistency and robustness of modern VQA models through a series of six fine-grained capability tests. In contrast to existi…

cs.CL20224 cited

Multi-Stage Episodic Control for Strategic Exploration in Text Games

Jens Tuyls, Shunyu Yao, Sham Kakade +1

Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards. The interplay of these two fac…

cs.CL20161 cited

Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

Nicholas Locascio, Karthik Narasimhan, Eduardo DeLeon +2

This paper explores the task of translating natural language queries into regular expressions which embody their meaning. In contrast to prior work, the proposed neural model does…