Rationale Extraction Task
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A Rationale Extraction Task is a legal argument mining task that identifies accepted claims and legal reasoning components that courts used to justify judicial decisions.
- AKA: Legal Rationale Identification Task, Judicial Reasoning Extraction Task, Decision Justification Mining Task.
- Context:
- It can typically extract Legal Winning Arguments with legal argument classification models.
- It can typically identify Legal Accepted Plaintiff Claims and Legal Accepted Defendant Claims through legal claim validation analysis.
- It can typically recognize Legal Reasoning Patterns with legal sequence labeling methods.
- It can often employ Legal Conditional Random Fields for legal structured prediction.
- It can often utilize Legal Hierarchical Attention Networks for legal multi-level text understanding.
- It can often apply Legal Argument Structure Parsers for legal reasoning chain identification.
- It can often integrate Legal Explainable AI Methods for legal transparency enhancement.
- It can range from being a Binary Rationale Extraction Task to being a Multi-Label Rationale Extraction Task, depending on its classification scheme.
- It can range from being a Sentence-Level Rationale Extraction Task to being a Paragraph-Level Rationale Extraction Task, depending on its extraction granularity.
- It can range from being a Extractive Rationale Extraction Task to being a Abstractive Rationale Extraction Task, depending on its generation approach.
- It can range from being a Rule-Based Rationale Extraction Task to being a Neural Rationale Extraction Task, depending on its methodological framework.
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- Examples:
- Competition Rationale Extraction Tasks, such as:
- COLIEE 2025 Pilot Task RE Subtask, extracting rationales from Japanese tort cases.
- CAIL 2022 Rationale Mining Challenge, identifying reasoning in Chinese judicial decisions.
- Applied Rationale Extraction Systems, such as:
- ...
- Competition Rationale Extraction Tasks, such as:
- Counter-Examples:
- Legal Judgment Prediction Task, which predicts outcomes rather than extracts rationales.
- Legal Document Summarization Task, which condenses rather than identifies specific reasoning.
- Legal Entity Recognition Task, which extracts entities rather than arguments.
- See: Legal Judgment Prediction Task, Legal Argument Mining Task, Explainable AI Task, Sequence Labeling Task, Conditional Random Field, Hierarchical Attention Network, Legal Natural Language Processing Task, Legal Information Extraction Task, Legal AI Task.