105 citations · 182 across the 8 of their papers we have counts for
9 papers
Return or Revise? Learning When Revision Helps Retrieval-Augmented QA
Nicholas Kashani Motlagh, Tim Anderson, Jeremy Gwinnup +1
We consider the decision of whether to return an existing draft answer or revise it using retrieved evidence, as in answer-revision systems. Draft confidence estimates whether the…
Findings of the MAGMaR 2026 Shared Task
Alexander Martin, Dengjia Zhang, Joel Brogan +7
This overview paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR). In this shared task partic…
Detecting Concrete Visual Tokens for Multimodal Machine Translation
Braeden Bowen, Vipin Vijayan, Scott Grigsby +2
The challenge of visual grounding and masking in multimodal machine translation (MMT) systems has encouraged varying approaches to the detection and selection of visually-grounded…
Adding Multimodal Capabilities to a Text-only Translation Model
Vipin Vijayan, Braeden Bowen, Scott Grigsby +2
While most current work in multimodal machine translation (MMT) uses the Multi30k dataset for training and evaluation, we find that the resulting models overfit to the Multi30k dat…
The Case for Evaluating Multimodal Translation Models on Text Datasets
Vipin Vijayan, Braeden Bowen, Scott Grigsby +2
A good evaluation framework should evaluate multimodal machine translation (MMT) models by measuring 1) their use of visual information to aid in the translation task and 2) their…
A Survey of Vision-Language Pre-training from the Lens of Multimodal Machine Translation
Jeremy Gwinnup, Kevin Duh
Large language models such as BERT and the GPT series started a paradigm shift that calls for building general-purpose models via pre-training on large datasets, followed by fine-t…
Learning When to Say "I Don't Know"
Nicholas Kashani Motlagh, Jim Davis, Tim Anderson +1
We propose a new Reject Option Classification technique to identify and remove regions of uncertainty in the decision space for a given neural classifier and dataset. Such existing…
An Empirical Exploration of Curriculum Learning for Neural Machine Translation
Xuan Zhang, Gaurav Kumar, Huda Khayrallah +6
Machine translation systems based on deep neural networks are expensive to train. Curriculum learning aims to address this issue by choosing the order in which samples are presente…
Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
Brian Thompson, Huda Khayrallah, Antonios Anastasopoulos +7
To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space…