Predicting Judicial Decisions of Criminal Cases from Thai Supreme Court Using Bi-directional GRU with Attention Mechanism

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

“…The same or similar task has also been studied with court cases in many other jurisdictions including France (S ¸ulea et al, 2017), Philippines (Virtucio et al, 2018), Turkey (Mumcuoglu et al, 2021), Thailand (Kowsrihawat et al, 2018), United Kingdom (Strickson and De La Iglesia, 2020), Germany (Urchs et al, 2021), and Switzerland (Niklaus et al, 2021). Apart from predicting court decisions, there is also work aiming to interpret (explain) the decisions of particular courts (Ye et al, 2018;Chalkidis et al, 2021c;Branting et al, 2021).…”

Rethinking the field of automatic prediction of court decisions

“…3 For description of earlier approaches in automatic prediction of court decision with and without using machine learning we refer to Ashley and Brüninghaus (2009) restricted set of courts. In this paper, we surveyed publications that use machine learning approaches and focus on case-law of the US Supreme Court (Sharma et al 2015;Katz et al 2017;Kaufman et al 2019), the French court of Cassation (Şulea et al 2017b;Sulea et al 2017a), the European Court of Human Rights (Aletras et al 2016;Liu and Chen 2017;Chalkidis et al 2019;Kaur and Bozic 2019;O'Sullivan and Beel 2019;Visentin et al 2019;Chalkidis et al 2020;Condevaux 2020;Medvedeva et al 2020a, b;Quemy and Wrembel 2020;Medvedeva et al 2021), Brazilian courts (Bertalan and Ruiz 2020;Lage-Freitas et al 2019), Indian courts (Bhilare et al 2019;Shaikh et al 2020;Malik et al 2021), UK courts (Strickson and De La Iglesia 2020), German courts (Waltl et al 2017), the Quebec Rental Tribunal (Salaün et al 2020) (Canada), the Philippine Supreme Court (Virtucio et al 2018), the Thai Supreme Court (Kowsrihawat et al 2018) and the Turkish Constitutional Court (Sert et al 2021). Many of these papers achieve a relatively high performance on their specific task using various machine learning techniques.…”

“…On the other hand, there are (fewer) studies which manually annotate data and use that as a basis for the categorisation. Kowsrihawat et al (2018) used the raw text to categorise (with an accuracy of 67%) the documents of the Thai Supreme Court on the basis of the facts of the case and the text related to the legal provisions in the cases such as murder, assault, theft, fraud and defamation using a range of statistical and neural methods. Medvedeva et al (2018), Medvedeva et al (2020a) categorised (with an accuracy of at most 75%) decisions of the ECtHR using only the facts of the case (i.e.…”

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

“…The same or similar task has also been studied with court cases in many other jurisdictions including France (S ¸ulea et al, 2017), Philippines (Virtucio et al, 2018), Turkey (Mumcuoglu et al, 2021), Thailand (Kowsrihawat et al, 2018), United Kingdom (Strickson and De La Iglesia, 2020), Germany (Urchs et al, 2021), and Switzerland (Niklaus et al, 2021). Apart from predicting court decisions, there is also work aiming to interpret (explain) the decisions of particular courts (Ye et al, 2018;Chalkidis et al, 2021c;Branting et al, 2021).…”