Argumentation-Driven Evidence Association in Criminal Cases (2021)

Abstract: Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.

## introduction previous work has put forward multiple legal assistant systems with various functions, such as searching relevant cases given the query @xcite , predicting the legal judgement @xcite , etc. despite promising results in this area, the research of judicial evidence in criminal cases has been omitted in recent years. the role of judicial evidence is to support several sub-claims in favour of conviction and the evidence description is an essential part of criminal judgement documents. however, the organization of evidence varies in different legal documents. the form of evidence association mainly includes collection form and argumentation-driven form as shown in figure 1 . in most current criminal judgement documents, the evidence is only listed in the form of a collection without giving explicit claims, which is regarded as collection form. however, evidence collection is divided into several subsets according to related claim only in around 5% criminal judgement documents, which is regarded as argumentation-driven form. as shown in figure 1 , evidence divided into the same subset could support the same claim and such kind of legal documents have better readability. inspired by this observation, we propose to study the problem of evidence association in this paper. evidence association is dividing a set of judicial evidence into several non-overlapping subsets according to their corresponding claims, improving the interpretability and legality of conviction. to our knowledge, there has been very limited research about evidence association in the legal field. evidence association could be treated as a clustering problem. existing short text clustering methods broadly fall into two categories: representationbased methods and semantic textual similarity methods @xcite . the representation-based methods concentrate on extracting rich semantic representation and then calculate cosine distance between text representations. the semantic textual similarity methods predict the distance between texts directly through supervised learning. however, the former methods perform poorly on the very short text and the latter methods require manually labelled data in the same field for supervised learning. we learn distance metric based on the probability supporting the same claim between evidence pairs directly on account of the short length of judicial evidence, which is regarded as an argumentation-driven method. another challenge is that the number of clusters in each case is various. in this paper, we use agglomerative hierarchical clustering to learn the stopping threshold to avoid specifying the number of clusters. our contributions of this paper are presented as follows: figure 1 : a real-word example of evidence descriptions in argumentation-driven form and collection form. the part before "prove that" is the evidence subset and the part after "prove that" is the corresponding claim. ## related works the evidence association task is motivated by previous research on the legal assistant system, especially by the work of improving the interpretability of charge prediction @xcite . to our knowledge, there has been very limited research about evidence associations in the legal field. one of the most related research work was done by @xcite . they use clustering techniques to identify argumentative sentences in legal documents, whereas it is a sentence-level task. as a part of argument mining, argument clustering aims to identify similar arguments. @xcite identifies similar arguments in online debates using semantic textual similarity. @xcite groups arguments that emphasize a specific aspect of a controversial topic. contextualized word embeddings methods are introduced in the classification and clustering of arguments in recent years @xcite . in this paper, we mainly used the bert @xcite and esim @xcite model to learn the distance metric between evidence pairs. ## methodology given a set of evidence @xmath0, e 2 , ..., e n } involved with a criminal case, we expect to split the each nonoverlapping subset of evidence e k proves the same claim c k . we firstly study the latent argumentationdriven evidence association in the case of lacking explicit claims. we also explored how to associate evidence more accurately in the case of giving the explicit claim set @xmath1, c 2 , ..., c k } involved in the criminal case. similarly, we define it as an explicit argumentation-driven evidence association. a suitable clustering method and a meaningful distance between evidence pairs are crucial for evidence association. ## conclusion in this paper, we propose a novel task of evidence association. the experiment results show that supervised methods significantly improve the clustering results even with a few training data. the clustering results have been greatly improved by introducing the information from explicit claims. since explicit claims are not given in most cases, we are now studying how to model the claims through the fact description of the case in order to take advantage of the improvement of explicit claims.