AT
A. Tocchetti
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
1 records found
1
Recent works explain the DNN models that perform image classification tasks following the "attribution, human-in-the-loop, extraction" workflow. However, little work has looked into such an approach for explaining DNN models for language or multimodal tasks. To address this gap, we propose a framework that explains and assesses the model utilizing both the categorical/numerical features and the text while optimizing the "attribution, human-in-the-loop, extraction" workflow. In particular, our framework deals with limited human resources, especially when domain experts are required for human-in-the-loop tasks. It provides insight regarding which set of data should the human-in-the-loop tasks be brought in. We share the results of applying this framework to a multimodal transformer that performs text classification tasks for compliance detection in the financial context.
...
...
Recent works explain the DNN models that perform image classification tasks following the "attribution, human-in-the-loop, extraction" workflow. However, little work has looked into such an approach for explaining DNN models for language or multimodal tasks. To address this gap, we propose a framework that explains and assesses the model utilizing both the categorical/numerical features and the text while optimizing the "attribution, human-in-the-loop, extraction" workflow. In particular, our framework deals with limited human resources, especially when domain experts are required for human-in-the-loop tasks. It provides insight regarding which set of data should the human-in-the-loop tasks be brought in. We share the results of applying this framework to a multimodal transformer that performs text classification tasks for compliance detection in the financial context.