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20162025
most citedIMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text

9 citations · 10 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Two-Level Meta-Rubrics for Evaluating Open-Ended Generation: GAMUT, a Benchmark for Factual Completeness

Xilun Chen, Zhaleh Feizollahi, Ross Goodwin +5

Rubric-based evaluation of open-ended generation faces a fundamental tension between expressiveness and reliability. Authoring a faithful rubric requires expressing the structure o…

cs.CL2024

Doppelgänger's Watch: A Split Objective Approach to Large Language Models

Shervin Ghasemlou, Ashish Katiyar, Aparajita Saraf +5

In this paper, we investigate the problem of "generation supervision" in large language models, and present a novel bicameral architecture to separate supervision signals from thei…

cs.CL2022

Navigating Connected Memories with a Task-oriented Dialog System

Seungwhan Moon, Satwik Kottur, Alborz Geramifard +1

Recent years have seen an increasing trend in the volume of personal media captured by users, thanks to the advent of smartphones and smart glasses, resulting in large media collec…

cs.CL20221 cited

Tell Your Story: Task-Oriented Dialogs for Interactive Content Creation

Satwik Kottur, Seungwhan Moon, Aram H. Markosyan +3

People capture photos and videos to relive and share memories of personal significance. Recently, media montages (stories) have become a popular mode of sharing these memories due…

cs.CL2021

SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations

Satwik Kottur, Seungwhan Moon, Alborz Geramifard +1

Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal en…

cs.CL2016

NN-grams: Unifying neural network and n-gram language models for Speech Recognition

Babak Damavandi, Shankar Kumar, Noam Shazeer +1

We present NN-grams, a novel, hybrid language model integrating n-grams and neural networks (NN) for speech recognition. The model takes as input both word histories as well as n-g…