Abstract: Receive a question via a graphical user interface (GUI), obtain a passage of text potentially relevant to the question, and receive, via the GUI, a selection of a number of question-answering models to be ensembled. Produce a plurality of answers to the question by running a plurality of question-answering models, consistent with the selection of the number of question-answering models to be ensembled, on the passage of text. Produce an ensembled answer by ensembling the plurality of answers according to their respective confidence scores. Display, via the GUI, the ensembled answer in context of the passage of text, with the ensembled answer visually marked in the passage of text. Optionally, repeat these steps for a second passage of text.

Abstract: Techniques for incremental domain adaptation are provided using iterative knowledge distillation to sequentially adapt a machine learning model to new tasks, and an external memory bank for storing the machine learning model parameters pertaining to the new tasks. In one aspect, a system for incremental domain adaptation includes: an iterative knowledge distillation module configured to adapt machine learning models to new tasks sequentially through multiple iterations of knowledge distillation; and an external memory bank configured to store parameters of the machine learning models pertaining to the new tasks. The external memory bank can employ adaptive memory allocation. A method for incremental domain adaptation using the present system is also provided.

Abstract: Receive a question via a graphical user interface (GUI), obtain a passage of text potentially relevant to the question, and receive, via the GUI, a selection of a number of question-answering models to be ensembled. Produce a plurality of answers to the question by running a plurality of question-answering models, consistent with the selection of the number of question-answering models to be ensembled, on the passage of text. Produce an ensembled answer by ensembling the plurality of answers according to their respective confidence scores. Display, via the GUI, the ensembled answer in context of the passage of text, with the ensembled answer visually marked in the passage of text. Optionally, repeat these steps for a second passage of text.