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2021 | OriginalPaper | Chapter

Cross-Domain Transfer of Generative Explanations Using Text-to-Text Models

Authors : Karl Fredrik Erliksson, Anders Arpteg, Mihhail Matskin, Amir H. Payberah

Published in: Natural Language Processing and Information Systems

Publisher: Springer International Publishing

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Abstract

Deep learning models based on the Transformers architecture have achieved impressive state-of-the-art results and even surpassed human-level performance across various natural language processing tasks. However, these models remain opaque and hard to explain due to their vast complexity and size. This limits adoption in highly-regulated domains like medicine and finance, and often there is a lack of trust from non-expert end-users. In this paper, we show that by teaching a model to generate explanations alongside its predictions on a large annotated dataset, we can transfer this capability to a low-resource task in another domain. Our proposed three-step training procedure improves explanation quality by up to 7% and avoids sacrificing classification performance on the downstream task, while at the same time reducing the need for human annotations.

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Code available at https://github.com/Peltarion/explainability_transfer.

Since all seq2seq models considered in this work have publicly released checkpoints from language model pre-training, this is used as starting point for step 2 in Fig. 1.

We use the dataset versions distributed through the ERASER benchmark [10].

The hyperparameter settings for the different models and training phases are available in the public code repository.

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Title: Cross-Domain Transfer of Generative Explanations Using Text-to-Text Models
Authors: Karl Fredrik Erliksson
Anders Arpteg
Mihhail Matskin
Amir H. Payberah
Publisher: Springer International Publishing
Book: Natural Language Processing and Information Systems
Print ISBN: 978-3-030-80598-2

Electronic ISBN: 978-3-030-80599-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-80599-9_8

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