Search Results for author: Scott Grigsby

Found 7 papers, 2 papers with code

Detecting Concrete Visual Tokens for Multimodal Machine Translation

no code implementations5 Mar 2024 Braeden Bowen, Vipin Vijayan, Scott Grigsby, Timothy Anderson, Jeremy Gwinnup

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 text tokens for masking.

Multimodal Machine Translation object-detection +3

Adding Multimodal Capabilities to a Text-only Translation Model

no code implementations5 Mar 2024 Vipin Vijayan, Braeden Bowen, Scott Grigsby, Timothy Anderson, Jeremy Gwinnup

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 dataset to an extreme degree.

Multimodal Machine Translation Translation

The Case for Evaluating Multimodal Translation Models on Text Datasets

no code implementations5 Mar 2024 Vipin Vijayan, Braeden Bowen, Scott Grigsby, Timothy Anderson, Jeremy Gwinnup

Therefore, we propose that MMT models be evaluated using 1) the CoMMuTE evaluation framework, which measures the use of visual information by MMT models, 2) the text-only WMT news translation task test sets, which evaluates translation performance against complex sentences, and 3) the Multi30k test sets, for measuring MMT model performance against a real MMT dataset.

Descriptive Image Captioning +2

Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities

1 code implementation31 Oct 2022 Nianzu Ma, Sahisnu Mazumder, Alexander Politowicz, Bing Liu, Eric Robertson, Scott Grigsby

Much of the existing work on text novelty detection has been studied at the topic level, i. e., identifying whether the topic of a document or a sentence is novel or not.

Novelty Detection Sentence

AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation

no code implementations17 Mar 2022 Bing Liu, Sahisnu Mazumder, Eric Robertson, Scott Grigsby

As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances.

Continual Learning

Self-Initiated Open World Learning for Autonomous AI Agents

no code implementations21 Oct 2021 Bing Liu, Eric Robertson, Scott Grigsby, Sahisnu Mazumder

As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can learn by themselves in a self-motivated and self-supervised manner rather than being retrained periodically on the initiation of human engineers using expanded training data.

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