Summary of Reframing Tax Law Entailment As Analogical Reasoning, by Xinrui Zou et al.
Reframing Tax Law Entailment as Analogical Reasoning
by Xinrui Zou, Ming Zhang, Nathaniel Weir, Benjamin Van Durme, Nils Holzenberger
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper redefines statutory reasoning as an analogy task, combining two instances of statutory reasoning into one. This approach increases the dataset size by two orders of magnitude, making it more challenging for Natural Language Processing (NLP) models to tackle. The authors demonstrate that this task is similarly difficult to original tasks, then solve the problem using a combination of retrieval and analogy models, showing progress on comparable work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand laws better by turning legal text into an analogy puzzle. It’s like comparing two sets of similar cases to figure out what the law says. This makes the task much bigger and harder for computers to solve. The researchers show that their approach is just as tricky as the original problem, then use a special combination of tools to make progress on this difficult task. |
Keywords
* Artificial intelligence * Natural language processing * Nlp
