1 code implementation • EMNLP 2021 • Nianzu Ma, Alexander Politowicz, Sahisnu Mazumder, Jiahua Chen, Bing Liu, Eric Robertson, Scott Grigsby
This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example.
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • 5 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.
1 code implementation • 31 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.
no code implementations • 17 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.
no code implementations • 21 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.