With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretation? In this opinion piece we reflect on the current state of interpretability evaluation research. We call for more clearly differentiating between different desired criteria an interpretation should satisfy, and focus on the faithfulness criteria. We survey the literature with respect to faithfulness evaluation, and arrange the current approaches around three assumptions, providing an explicit form to how faithfulness is “defined” by the community. We provide concrete guidelines on how evaluation of interpretation methods should and should not be conducted. Finally, we claim that the current binary definition for faithfulness sets a potentially unrealistic bar for being considered faithful. We call for discarding the binary notion of faithfulness in favor of a more graded one, which we believe will be of greater practical utility.
|Title of host publication||ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||8|
|State||Published - 2020|
|Event||58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States|
Duration: 5 Jul 2020 → 10 Jul 2020
|Name||Proceedings of the Annual Meeting of the Association for Computational Linguistics|
|Conference||58th Annual Meeting of the Association for Computational Linguistics, ACL 2020|
|Period||5/07/20 → 10/07/20|
Bibliographical noteFunding Information:
This project has received funding from the Eu-ropoean Research Council (ERC) under the Eu-ropoean Union’s Horizon 2020 research and innovation programme, grant agreement No. 802774 (iEXTRACT).
We thank Yanai Elazar for welcome input on the presentation and organization of the paper. We also thank the reviewers for additional feedback and pointing to relevant literature in HCI and IUI. This project has received funding from the Europoean Research Council (ERC) under the Europoean Union's Horizon 2020 research and innovation programme, grant agreement No. 802774 (iEXTRACT).
© 2020 Association for Computational Linguistics