Search Results for author: Kate Sanders

Found 6 papers, 0 papers with code

Tur[k]ingBench: A Challenge Benchmark for Web Agents

no code implementations18 Mar 2024 Kevin Xu, Yeganeh Kordi, Kate Sanders, Yizhong Wang, Adam Byerly, Jack Zhang, Benjamin Van Durme, Daniel Khashabi

We evaluate the performance of state-of-the-art models, including language-only, vision-only, and layout-only models, and their combinations, on this benchmark.

TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning

no code implementations29 Feb 2024 Kate Sanders, Nathaniel Weir, Benjamin Van Durme

It is challenging to perform question-answering over complex, multimodal content such as television clips.

Question Answering Video Understanding

Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic

no code implementations22 Feb 2024 Nathaniel Weir, Kate Sanders, Orion Weller, Shreya Sharma, Dongwei Jiang, Zhengping Jiang, Bhavana Dalvi Mishra, Oyvind Tafjord, Peter Jansen, Peter Clark, Benjamin Van Durme

Contemporary language models enable new opportunities for structured reasoning with text, such as the construction and evaluation of intuitive, proof-like textual entailment trees without relying on brittle formal logic.

Formal Logic Knowledge Distillation +2

MultiVENT: Multilingual Videos of Events with Aligned Natural Text

no code implementations6 Jul 2023 Kate Sanders, David Etter, Reno Kriz, Benjamin Van Durme

Everyday news coverage has shifted from traditional broadcasts towards a wide range of presentation formats such as first-hand, unedited video footage.

Information Retrieval Retrieval +1

Ambiguous Images With Human Judgments for Robust Visual Event Classification

no code implementations6 Oct 2022 Kate Sanders, Reno Kriz, Anqi Liu, Benjamin Van Durme

However, humans are frequently presented with visual data that they cannot classify with 100% certainty, and models trained on standard vision benchmarks achieve low performance when evaluated on this data.

Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking

no code implementations20 Jul 2020 Kate Sanders, Michael Danielczuk, Jeffrey Mahler, Ajay Tanwani, Ken Goldberg

A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce.

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