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Cunchao Tu

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7 papers

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AAAI Conference 2020 Conference Paper

Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction

Haoxi Zhong
Yuzhong Wang
Cunchao Tu
Tianyang Zhang
Zhiyuan Liu
Maosong Sun

Legal Judgment Prediction (LJP) aims to predict judgment results according to the facts of cases. In recent years, LJP has drawn increasing attention rapidly from both academia and the legal industry, as it can provide references for legal practitioners and is expected to promote judicial justice. However, the research to date usually suffers from the lack of interpretability, which may lead to ethical issues like inconsistent judgments or gender bias. In this paper, we present QAjudge, a model based on reinforcement learning to visualize the prediction process and give interpretable judgments. QAjudge follows two essential principles in legal systems across the world: Presumption of Innocence and Elemental Trial. During inference, a Question Net will select questions from the given set and an Answer Net will answer the question according to the fact description. Finally, a Predict Net will produce judgment results based on the answers. Reward functions are designed to minimize the number of questions asked. We conduct extensive experiments on several realworld datasets. Experimental results show that QAjudge can provide interpretable judgments while maintaining comparable performance with other state-of-the-art LJP models. The codes can be found from https: //github. com/thunlp/QAjudge.

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AAAI Conference 2020 Conference Paper

JEC-QA: A Legal-Domain Question Answering Dataset

Haoxi Zhong
Chaojun Xiao
Cunchao Tu
Tianyang Zhang
Zhiyuan Liu
Maosong Sun

We present JEC-QA, the largest question answering dataset in the legal domain, collected from the National Judicial Examination of China. The examination is a comprehensive evaluation of professional skills for legal practitioners. College students are required to pass the examination to be certiﬁed as a lawyer or a judge. The dataset is challenging for existing question answering methods, because both retrieving relevant materials and answering questions require the ability of logic reasoning. Due to the high demand of multiple reasoning abilities to answer legal questions, the state-of-the-art models can only achieve about 28% accuracy on JEC-QA, while skilled humans and unskilled humans can reach 81% and 64% accuracy respectively, which indicates a huge gap between humans and machines on this task. We will release JEC-QA and our baselines to help improve the reasoning ability of machine comprehension models. You can access the dataset from http: //jecqa. thunlp. org/.

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AAAI Conference 2018 Conference Paper

Chinese LIWC Lexicon Expansion via Hierarchical Classification of Word Embeddings with Sememe Attention

Xiangkai Zeng
Cheng Yang
Cunchao Tu
Zhiyuan Liu
Maosong Sun

Linguistic Inquiry and Word Count (LIWC) is a word counting software tool which has been used for quantitative text analysis in many ﬁelds. Due to its success and popularity, the core lexicon has been translated into Chinese and many other languages. However, the lexicon only contains several thousand of words, which is deﬁcient compared with the number of common words in Chinese. Current approaches often require manually expanding the lexicon, but it often takes too much time and requires linguistic experts to extend the lexicon. To address this issue, we propose to expand the LIWC lexicon automatically. Speciﬁcally, we consider it as a hierarchical classiﬁcation problem and utilize the Sequence-to-Sequence model to classify words in the lexicon. Moreover, we use the sememe information with the attention mechanism to capture the exact meanings of a word, so that we can expand a more precise and comprehensive lexicon. The experimental results show that our model has a better understanding of word meanings with the help of sememes and achieves signiﬁcant and consistent improvements compared with the state-of-the-art methods. The source code of this paper can be obtained from https: //github. com/thunlp/Auto CLIWC.

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IJCAI Conference 2017 Conference Paper

Fast Network Embedding Enhancement via High Order Proximity Approximation

Cheng Yang
Maosong Sun
Zhiyuan Liu
Cunchao Tu

Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently. In this paper, we summarize most existing NRL methods into a unified two-step framework, including proximity matrix construction and dimension reduction. We focus on the analysis of proximity matrix construction step and conclude that an NRL method can be improved by exploring higher order proximities when building the proximity matrix. We propose Network Embedding Update (NEU) algorithm which implicitly approximates higher order proximities with theoretical approximation bound and can be applied on any NRL methods to enhance their performances. We conduct experiments on multi-label classification and link prediction tasks. Experimental results show that NEU can make a consistent and significant improvement over a number of NRL methods with almost negligible running time on all three publicly available datasets.

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TIST Journal 2017 Journal Article

PRISM

Cunchao Tu
Zhiyuan Liu
Huanbo Luan
Maosong Sun

Profession is an important social attribute of people. It plays a crucial role in commercial services such as personalized recommendation and targeted advertising. In practice, profession information is usually unavailable due to privacy and other reasons. In this article, we explore the task of identifying user professions according to their behaviors in social media. The task confronts the following challenges that make it non-trivial: how to incorporate heterogeneous information of user behaviors, how to effectively utilize both labeled and unlabeled data, and how to exploit community structure. To address these challenges, we present a framework called Profession Identification in Social Media. It takes advantage of both personal information and community structure of users in the following aspects: (1) We present a cascaded two-level classifier with heterogeneous personal features to measure the confidence of users belonging to different professions. (2) We present a multi-training process to take advantages of both labeled and unlabeled data to enhance classification performance. (3) We design a profession identification method synthetically considering the confidences from personal features and community structure. We collect a real-world dataset to conduct experiments, and experimental results demonstrate the significant effectiveness of our method compared with other baseline methods. By applying prediction on large-scale users, we also analyze characteristics of microblog users, finding that there are significant diversities among users of different professions in demographics, social network structures, and linguistic styles.

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IJCAI Conference 2017 Conference Paper

TransNet: Translation-Based Network Representation Learning for Social Relation Extraction

Cunchao Tu
Zhengyan Zhang
Zhiyuan Liu
Maosong Sun

Conventional network representation learning (NRL) models learn low-dimensional vertex representations by simply regarding each edge as a binary or continuous value. However, there exists rich semantic information on edges and the interactions between vertices usually preserve distinct meanings, which are largely neglected by most existing NRL models. In this work, we present a novel Translation-based NRL model, TransNet, by regarding the interactions between vertices as a translation operation. Moreover, we formalize the task of Social Relation Extraction (SRE) to evaluate the capability of NRL methods on modeling the relations between vertices. Experimental results on SRE demonstrate that TransNet significantly outperforms other baseline methods by 10% to 20% on hits@1. The source code and datasets can be obtained from https: //github. com/thunlp/TransNet.

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IJCAI Conference 2016 Conference Paper

Max-Margin DeepWalk: Discriminative Learning of Network Representation

Cunchao Tu
Weicheng Zhang
Zhiyuan Liu
Maosong Sun

DeepWalk is a typical representation learning method that learns low-dimensional representations for vertices in social networks. Similar to other network representation learning (NRL) models, it encodes the network structure into vertex representations and is learnt in unsupervised form. However, the learnt representations usually lack the ability of discrimination when applied to machine learning tasks, such as vertex classification. In this paper, we overcome this challenge by proposing a novel semi-supervised model, max-margin DeepWalk (MMDW). MMDW is a unified NRL framework that jointly optimizes the max-margin classifier and the aimed social representation learning model. Influenced by the max-margin classifier, the learnt representations not only contain the network structure, but also have the characteristic of discrimination. The visualizations of learnt representations indicate that our model is more discriminative than unsupervised ones, and the experimental results on vertex classification demonstrate that our method achieves a significant improvement than other state-of-the-art methods.

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