2.4k citations · 3k across the 56 of their papers we have counts for
6 papers · 1 filter
Few-shot Object Grounding and Mapping for Natural Language Robot Instruction Following
Valts Blukis, Ross A. Knepper, Yoav Artzi
We study the problem of learning a robot policy to follow natural language instructions that can be easily extended to reason about new objects. We introduce a few-shot language-co…
Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop
Matthew Marge, Carol Espy-Wilson, Nigel Ward
With robotics rapidly advancing, more effective human-robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be pa…
Revisiting Few-sample BERT Fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar +2
This paper is a study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cau…
What is Learned in Visually Grounded Neural Syntax Acquisition
Noriyuki Kojima, Hadar Averbuch-Elor, Alexander M. Rush +1
Visual features are a promising signal for learning bootstrap textual models. However, blackbox learning models make it difficult to isolate the specific contribution of visual com…
Evaluating Models' Local Decision Boundaries via Contrast Sets
Matt Gardner, Yoav Artzi, Victoria Basmova +23
Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluation…
Retouchdown: Adding Touchdown to StreetLearn as a Shareable Resource for Language Grounding Tasks in Street View
Harsh Mehta, Yoav Artzi, Jason Baldridge +2
The Touchdown dataset (Chen et al., 2019) provides instructions by human annotators for navigation through New York City streets and for resolving spatial descriptions at a given l…