Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing 2023
DOI: 10.18653/v1/2023.emnlp-main.594
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From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification

Shanshan Xu,
Santosh T.y.s.s,
Oana Ichim
et al.

Abstract: In legal NLP, Case Outcome Classification (COC) must not only be accurate but also trustworthy and explainable. Existing work in explainable COC has been limited to annotations by a single expert. However, it is well-known that lawyers may disagree in their assessment of case facts. We hence collect a novel dataset RAVE: Rationale Variation in ECHR 1 , which is obtained from two experts in the domain of international human rights law, for whom we observe weak agreement. We study their disagreements and build a… Show more

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Cited by 2 publications
(3 citation statements)
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“…For example, if you want to know if and how a neural network's representations relate to a dependency parse of the input sentence, investigating the network's use of tokens in a sentence is a wise choice. In legal NLP, however, despite the recent rejuvenation of efforts in the field (Xu et al, 2023;Habernal et al, 2023), there is no generally accepted legal theory about how individual words or sentences affect the outcome of a case. We further elaborate on the related work in §9.…”
Section: Precedent-based Interpretabilitymentioning
confidence: 99%
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“…For example, if you want to know if and how a neural network's representations relate to a dependency parse of the input sentence, investigating the network's use of tokens in a sentence is a wise choice. In legal NLP, however, despite the recent rejuvenation of efforts in the field (Xu et al, 2023;Habernal et al, 2023), there is no generally accepted legal theory about how individual words or sentences affect the outcome of a case. We further elaborate on the related work in §9.…”
Section: Precedent-based Interpretabilitymentioning
confidence: 99%
“…This makes our work different from prior research in two aspects. (1) Where the existing Legal AI work focuses on providing explanations by identifying relevant tokens (Xu et al, 2023), sentences (Malik et al, 2021) or paragraphs (Chalkidis et al, 2021), of the facts of the case, we find the entire relevant cases. (2) Instead of looking inside the input to the model, i.e., at the facts, we look at the training data of the model.…”
Section: Related Workmentioning
confidence: 99%
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