F1 Is Not Enough! Models and Evaluation towards User-Centered Explainable Question Answering

By Hendrik Schuff, Heike Adel, Ngoc Thang Vu. 2020

23 citations recorded by the source corpus.

Explainable question answering systems predict an answer together with an explanation showing why the answer has been selected. The goal is to enable users to assess the correctness of the system and understand its reasoning process. However, we show that current models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience. As a remedy, we propose a hierarchical model and a new regularization term to strengthen the answer-explanation coupling as well as two evaluation scores to quantify the coupling. We conduct experiments on the HOTPOTQA benchmark data set and perform a user study. The user study shows that our models increase the ability of the users to judge the correctness of the system and that scores like F1 are not enough to estimate the usefulness of a model in a practical setting with human users. Our scores are better aligned with user experience, making them promising candidates for model selection.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Natural Language Processing

Citations in the corpus

Measuring Association Between Labels and Free-Text Rationales (2020, methodology): “Prior work proposing models (Jain et al., 2020; Schuff et al., 2020; Jacovi and Goldberg, 2021) and evaluations (DeY-oung et al., 2020) of faithful explanation focus on extractive rationales and generally rely on the sufﬁ-ciency assumption.”
What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models (2021, methodology): “This emerging interpretability is considerably different from methods that explicitly train models to output explanations (Perez et al., 2019; Schuff et al., 2020).”
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing (2021, background): “[112] Hendrik Schuff, Heike Adel, and Ngoc Thang Vu.”
Teach Me to Explain: A Review of Datasets for Explainable NLP (2021, background): “Such examples can improve E X NLP models’ by providing supervision on what is not a correct explanation (Schuff et al., 2020).”