形象正义：结合定性评估与Transformer检测印度语司法判决中的隐喻
Figurative Justice: Detecting metaphors in Hindi judgements with qualitative assessment and transformers
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中文总结 AI 辅助
本研究针对低资源语言印度语，构建了HiLeMe语料库并采用mBERT及Transformer架构，实现了印度语司法判决中的隐喻检测，为低资源语言法律论述的自动化模型发展提供了支撑。
中文摘要 AI 辅助
隐喻是用于概念映射的词语的形象化用法。在法律语境中，隐喻检测至关重要，因为隐喻是构建法律意义与概念的有说服力的司法手段，会产生重大后果。法官、律师及立法者在法律论述中采用的隐喻框架，会对个人产生实时影响，并影响司法决策、论证及法律解释，在人权侵权案件中尤为关键——此类案件中，语言决定惩罚 severity、公众认知及司法结果。尽管针对英语、西班牙语、波兰语、立陶宛语等主要语言的自动隐喻检测已助力理解隐喻性语言使用的内在意图，但针对印度语等低资源语言的相关尝试仍属空白。印度语标注法律语料库的匮乏，使得开发NLP模型以检测司法程序中的隐喻变得困难。在印度语境中，卷积神经网络（CNNs）已被用于保释判决的分类，但尚无专为隐喻检测设计的现有模型。本研究通过从印度语法律数据语料库（HLDC）中分离判决，构建了印度语法律隐喻语料库（HiLeMe），法律专家采用MIPVU schema对HiLeMe进行标注以分类隐喻结构。我们将mBERT下游应用于印度语法律隐喻检测任务，构建了基于Transformer的隐喻检测架构——该架构在法律分类任务中表现优于传统模型。此模型为解读司法决策提供了司法心理洞察，本研究有助于推进印度语等低资源语言法律论述中的自动化模型，并设想其将被应用于印度22种 scheduled languages。
英文摘要
Metaphors are figurative use of words for conceptual mapping. Metaphor detection in the legal context has been crucial as metaphors are persuasive juridical means of creating legal meaning and concepts resulting in significant consequences. Metaphorical framing in legal discourse by judges, lawyers, and legislators brings about real-time implications upon individuals and influences judicial decision-making, argumentation and interpretation of laws. This is crucial in Human Rights infringement cases where language determines severity of punishment, public perception and judicial outcomes. While automatic metaphor detection in major languages like English, Spanish, Polish, Lithuanian have aided in understanding inherent intentions of metaphorical use of language, there is no such attempt in low-resource languages like Hindi. The dearth of annotated legal corpora in Hindi makes it difficult to develop NLP models and detect metaphors in judicial proceedings. In the Indian context, Convolutional Neural Networks (CNNs) have been used for classification of bail judgements, however there are no existing models designed for metaphor detection. We present a Hindi Legal Metaphor Corpus (HiLeMe) by isolating judgements from Hindi Legal Data Corpus (HLDC). Legal experts annotated HiLeMe to classify metaphorical constructions using the MIPVU schema. We downstreamed an mBERT on Hindi legal metaphor detection task. We built a transformer-based architecture for metaphor detection that are known to outperform traditional models in legal classification tasks. This model provides insights into the judicial psyche for decoding judicial decisions. Our research contributes to advancing automated models in legal discourse in low-resource languages like Hindi and envisages adoption into 22 Indian schedule languages.
发表机构
- DigiTS, University of Tartu（塔尔图大学 DigiTS）
- The West Bengal National University of Juridical Sciences (NUJS)（西孟加拉邦国家司法科学大学）
- School of Languages and Linguistics, Jadavpur University（贾达普大学语言与语言学学院）
机构由 AI 辅助整理，请以论文原文为准。
