Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAutomated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language
استخراج آلي من قرارات الأحكام من قضايا المحكمة في اللغة العبرية
322
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نقدم مهمة استخراج العقوبة الآلي (القرد) في قرارات الأحكام من قضايا المحكمة الجنائية في العبرية.سيمكن معالجة القرد من تحديد أنماط الحكم وتشكل حجر خطوة هامة للعديد من تطبيقات NLP القانونية المتابعة باللغة العبرية، بما في ذلك التنبؤ بقرارات الأحكام.نحن نحذر مجموعة بيانات من قرارات الحكم الجنسي للاعتداء الجنسي ومجموعة بيانات التقييم المشروح يدويا، وتنفيذ النماذج القائمة على القواعد والإشراف.نجد أنه في حين أن النماذج الخاضعة للإشراف يمكن أن تحدد الجملة التي تحتوي على العقوبة بدقة جيدة، فإن النهج القائمة على القواعد تتفوق عليها في مهمة القرد الكامل.نستنتج من خلال تقديم أول تحليل لأنماط الأحكام في DataSet وتحليل أخطاء النماذج المشتركة، مما يشير إلى طرق العمل في المستقبل، مثل التمييز بين الاختبار وعقوبة السجن الفعلية.سنقوم بإتاحة جميع مواردنا عند الطلب، بما في ذلك البيانات والتصفية والنماذج القياسية الأولى.
We present the task of Automated Punishment Extraction (APE) in sentencing decisions from criminal court cases in Hebrew. Addressing APE will enable the identification of sentencing patterns and constitute an important stepping stone for many follow up legal NLP applications in Hebrew, including the prediction of sentencing decisions. We curate a dataset of sexual assault sentencing decisions and a manually-annotated evaluation dataset, and implement rule-based and supervised models. We find that while supervised models can identify the sentence containing the punishment with good accuracy, rule-based approaches outperform them on the full APE task. We conclude by presenting a first analysis of sentencing patterns in our dataset and analyze common models' errors, indicating avenues for future work, such as distinguishing between probation and actual imprisonment punishment. We will make all our resources available upon request, including data, annotation, and first benchmark models.
References used
https://aclanthology.org/
rate research
Read More
Interpretability or explainability is an emerging research field in NLP. From a user-centric point of view, the goal is to build models that provide proper justification for their decisions, similar to those of humans, by requiring the models to sati
sfy additional constraints. To this end, we introduce a new application on legal text where, contrary to mainstream literature targeting word-level rationales, we conceive rationales as selected paragraphs in multi-paragraph structured court cases. We also release a new dataset comprising European Court of Human Rights cases, including annotations for paragraph-level rationales. We use this dataset to study the effect of already proposed rationale constraints, i.e., sparsity, continuity, and comprehensiveness, formulated as regularizers. Our findings indicate that some of these constraints are not beneficial in paragraph-level rationale extraction, while others need re-formulation to better handle the multi-label nature of the task we consider. We also introduce a new constraint, singularity, which further improves the quality of rationales, even compared with noisy rationale supervision. Experimental results indicate that the newly introduced task is very challenging and there is a large scope for further research.
Knowing the vowels in the Hebrew language is one of the most important obstacles faced by learners of the Hebrew language, because of the complexity compared to their counterparts in the Arabic language. I have worked hard, in my research, on
simplifying them, as far as possible, for the Arab recipients through comparing them to their counterparts in the Arabic language. This research may show us that most Vowels in Hebrew have similar counterparts in Arabic, but Arab linguist did not allocate an independent vowel for each case as Hebrew linguists did, which suggests to the neophyte that the number of the symbols of vowels in Hebrew is larger than the number of those in Arabic.
The Statute of the International Criminal Court authorized the appeal of
its judgments in two ways: ordinary, an appeal, an extraordinary review
of judgments, and in the eyes of both appeals the Appeals Chamber of
the International Criminal Court. The international legislator organized
the grounds, procedures, provisions and effects of the appeal, Much
more than what was prescribed in national legislation.
As it has been unveiled that pre-trained language models (PLMs) are to some extent capable of recognizing syntactic concepts in natural language, much effort has been made to develop a method for extracting complete (binary) parses from PLMs without
training separate parsers. We improve upon this paradigm by proposing a novel chart-based method and an effective top-K ensemble technique. Moreover, we demonstrate that we can broaden the scope of application of the approach into multilingual settings. Specifically, we show that by applying our method on multilingual PLMs, it becomes possible to induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, attaining performance superior or comparable to that of unsupervised PCFGs. We also verify that our approach is robust to cross-lingual transfer. Finally, we provide analyses on the inner workings of our method. For instance, we discover universal attention heads which are consistently sensitive to syntactic information irrespective of the input language.
Legal texts routinely use concepts that are difficult to understand. Lawyers elaborate on the meaning of such concepts by, among other things, carefully investigating how they have been used in the past. Finding text snippets that mention a particula
r concept in a useful way is tedious, time-consuming, and hence expensive. We assembled a data set of 26,959 sentences, coming from legal case decisions, and labeled them in terms of their usefulness for explaining selected legal concepts. Using the dataset we study the effectiveness of transformer models pre-trained on large language corpora to detect which of the sentences are useful. In light of models' predictions, we analyze various linguistic properties of the explanatory sentences as well as their relationship to the legal concept that needs to be explained. We show that the transformer-based models are capable of learning surprisingly sophisticated features and outperform the prior approaches to the task.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
