Few-Shot Charge Prediction with Discriminative Legal Attributes

By Zikun Hu, Xiang Li, Cunchao Tu, Zhiyuan Liu, Maosong Sun. 2018

250 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Zixuan Ke
2. Hu Xu
3. Ilias Chalkidis
4. Bin Liu
5. Bing Liu
6. Lei Shu
7. Ion Androutsopoulos
8. Jey Han Lau

Citations in the corpus

• Continual Training of Language Models for Few-Shot Learning (2022, methodology): “…over-fitting fall in three main families: (i) model-based methods try to reduce the hypothesis space of the few-shot task (Triantafillou et al., 2017; Hu et al., 2018), (ii) data-based methods try to augment additional data to the few-shot set (Benaim and Wolf, 2018; Gao et al., 2020), and (iii)…”
• Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation (2022, methodology): “…2020); 160 (ii) model-based methods reduce the hypothesis 161 space using prior knowledge (Rezende et al., 2016; 162 Triantafillou et al., 2017; Hu et al., 2018); and 163 (iii) algorithm-based methods try to find a more 164 suitable strategy to search for the best hypothesis in 165 the whole…”
• LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 (2021, methodology): “…over-2 fitting include: ( i ) model-based methods that explore how to reduce the hypothesis space of the few-shot task (Triantafillou et al., 2017; Hu et al., 2018), ( ii ) data-based methods that try to augment additional data to the few-shot set (Benaim & Wolf, 2018; Gao et al., 2020b), and (…”
• Generalizing from a Few Examples (2019, methodology): “In [61], two natural language processing tasks on legal texts are solved together: charge prediction and legal attribute prediction.”
• Few-Shot Semantic Parsing for New Predicates (2021, background): “It categorizes the models into multitask learning (Hu et al., 2018), embedding learning (Snell et al., 2017; Vinyals et al., 2016), learning with external memory (Lee and Choi, 2018; Sukhbaatar et al., 2015), and generative modeling (Reed et al., 2017) in terms of what prior knowledge is used.”
• Legal Judgment Prediction via Topological Learning (2018, background): “LJP has been studied for decades (Kort, 1957; Ulmer, 1963; Nagel, 1963; Keown, 1980; Segal, 1984; Lauderdale and Clark, 2012; Ye et al., 2018; Hu et al., 2018), and most existing works formalize LJP as a text classification task.”
• Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis? (2020, background): “ NLP research. Having said this, we acknowledge that similar datasets have been constructed and used by others (esp. Xiao et al. (2018)), including in major NLP conferences (e.g. Zhong et al. (2018), Hu et al. (2018)). However, this should never be taken as a waiver for data ethic considerations. Al”
• CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction (2018, methodology): “Inspired by the success of deep learning techniques on natural language processing tasks, researchers attempt to employ neural models to handle judgment prediction task under the text clas-sification framework (Luo et al., 2017; Hu et al., 2018).”
• CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain (2019, background): “With the development of deep learning, more researchers pay much efforts on predicting the judgment result of legal cases ( Luo et al., 2017; Hu et al., 2018; Zhong et al., 2018a; Chalkidis et al., 2019; Jiang et al., 2018; Yang et al., 2019).”
• A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities (2022, methodology): “This idea was applied to automatic charge prediction [125] by adding several discriminative attributes, eliminating attribute irrelevant features, and using attribute-relevant features for accusation prediction.”
• A Survey on Text Classification: From Shallow to Deep Learning (2020, background): “Hu et al. [152] propose an attention mechanism based on category attributes for solving the imbalance of the number of various charges which contain few-shot charges.”
• Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases (2021, background): “, 2019) and for Chinese criminal court cases (Luo et al., 2017; Hu et al., 2018; Zhong et al., 2018), but there is no precedent of work investigating the justification of the models’ decisions.”
• How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020, background): “With the development of the neural network, many researchers begin to explore LJP using deep learning technology (Hu et al., 2018; Wang et al., 2019; Li et al., 2019b; Liu et al., 2019b; Li et al., 2019a; Kang et al., 2019).”
• Neural Legal Judgment Prediction in English (2019, methodology): “Here, we focus on legal judgment prediction , where given a text describing the facts of a legal case, the goal is to predict the court’s outcome (Aletras et al., 2016; S¸ulea et al., 2017; Luo et al., 2017; Zhong et al., 2018; Hu et al., 2018).”
• A Survey on Text Classification: From Traditional to Deep Learning (2020, unknown): “[191] propose an attention mechanism based on”