KGI: An Integrated Framework for Knowledge Intensive Language Tasks

• URL: http://arxiv.org/abs/2204.03985v1
• Date: Fri, 8 Apr 2022 10:36:21 GMT
• Title: KGI: An Integrated Framework for Knowledge Intensive Language Tasks
• Authors: Md Faisal Mahbub Chowdhury, Michael Glass, Gaetano Rossiello, Alfio Gliozzo and Nandana Mihindukulasooriya
• Abstract summary: In this paper, we propose a system based on an enhanced version of this approach for other knowledge intensive language tasks. Our system achieves results comparable to the best models in the KILT leaderboards.
• Score: 16.511913995069097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a recent work, we presented a novel state-of-the-art approach to zero-shot slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. In this paper, we propose a system based on an enhanced version of this approach where we train task specific models for other knowledge intensive language tasks, such as open domain question answering (QA), dialogue and fact checking. Our system achieves results comparable to the best models in the KILT leaderboards. Moreover, given a user query, we show how the output from these different models can be combined to cross-examine each other. Particularly, we show how accuracy in dialogue can be improved using the QA model. A short video demonstrating the system is available here - \url{https://ibm.box.com/v/kgi-interactive-demo} .

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