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The approach targets the United States MultiState Bar Examination (MBE), a multi-choice 200-question test for prospective lawyers, and is evaluated on this real exam setting. The system achieves strong results without external knowledge by curating a small, MBE-aligned corpus and outperforming a TFIDF baseline. Performance also remains competitive when adapted to legal textual entailment evaluation.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/solving-bar-exam-questions-with-deep-neural-networks/320275/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/solving-bar-exam-questions-with-deep-neural-networks/320275.png","ImageObject",300,407,{"name":92,"@type":93},"WPS_1786070896","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the paper address?","Question",{"text":113,"@type":114},"The paper addresses solving a Bar Examination written in natural language using deep neural networks for question answering.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What exam and evaluation setup are used?",{"text":118,"@type":114},"The system is evaluated on the United States MultiState Bar Examination (MBE), a 200-question multi-choice exam, and performance is also tested under a legal textual entailment evaluation.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the proposed system differ from simpler baselines?",{"text":122,"@type":114},"The system achieves good performance without relying on external knowledge and is shown to beat a TFIDF-based baseline.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},320275,1790015964,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},549768072016,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Solving Bar Exam Questions with Deep Neural Networks  \nAdebayo Kolawole John  \nDepartment of Computer Science, University of Torino Corso Svizzera 185 Torino, 10149, Italy  \n[collawolley3@yahoo.com](collawolley3@yahoo.com)  \nLuigi Di Caro  \nDepartment of Computer Science, University of Torino Corso Svizzera 185 Torino, 10149, Italy  \ndicaro@di.unito.it  \nGuido Boella  \nDepartment of Computer Science, University of Torino Corso Svizzera 185 Torino, 10149, Italy  \nguido@di.unito.it  \nABSTRACT  \nIn this paper, we present a system which solves a Bar Examination written in Natural Language. The proposed system exploits the recent techniques in Deep Neural Networks which have shown promise in many Natural Language Processing (NLP) applications. We evaluate our system on a real Legal Bar Examination, the United States MultiState Bar Examination (MBE), which is a multi-choice 200-questions exam for aspiring lawyers. We show that our system achieves good performance without relying on any external knowledge. Our work comes with an added e􀀋ort of curating a small corpus, following similar question answering datasets from the well-known MBE examination. The proposed system beats a TFIDF-based baseline, while showing a strong performance when modi􀀌ed for a legal Textual Entailment evaluation.  \n1. INTRODUCTION  \nMany tasks in Natural Language Processing (NLP) involve generation of semantic representation for proper text understanding. For example, tasks like Textual Entailment [5] and Question Answering [11, 31] involve deep semantic understanding of the text since a popular approach like the Bag of Words (BOW) has limitations due to natural language ambiguity.  \nQuestion Answering (QA) tasks follow the Human learning and testing process. For instance, a student reads a course note in order to obtain some facts and background knowledge. The student then answers any question based on the facts available to him. This is the main essence of learning, which is about 'committing to memory' and 'generalizing' to new events. Even though learning seems tobe a natural phenomenon to humans, it is nevertheless still a challenging goal for computers to replicate. Researchers working in the Computer Science 􀀌eld of Machine Learning (ML) often employ methods to analyze existing data in order to predict the likelihood of uncertain outcomes. These methods usually produce results that approximate human capabilities [19] .  \nThe term ML is actually a broad term used to describe supervised or unsupervised approaches for making the computer identify patterns in our data. Usually, a human hand-  \nIn: Proceedings of the Second Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2017), June 16, 2017, London, UK.  \nCopyright © 2017 held by the authors. Copying permitted for private and academic purposes.  \nPublished at [http://ceur-ws.org](http://ceur-ws.org)  \ncrafts some features from the data, and the extracted features are then shown to the algorithm for it to learn the latent discriminating features. Finally, the algorithm learns to predict the outcome of an unseen event. Neural Networks (NN) [8] are now extensively used by researchers because they o􀀋er a higher representational power. NN try to mimic the cognitive system of the human. They have a lot of interconnected nodes. Each node receives some inputs from the lower layer nodes, performs a computation on the input by using some non-linear functions, and lastly, the node transmits its output to the nodes in the layer above it. Such a network with many interconnecting layers stacked is called a Deep Neural Network (DNN) [24] .  \nWhen performed by a human, QA requires some form of cognitive abilities such as reasoning, meta-cognition, the contextual perception of abstract concepts, intelligence, and language comprehension. Although machines are yet to replicate a strong cognitive ability like a human, nevertheless, thenon-cognitive computational techniques that employ heuristics an","cbCail3u9PFdB328","https://ap.wps.com/l/cbCail3u9PFdB328","pdf",409147,"English","# Introduction\n# Related Work","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses solving a Bar Examination written in natural language using deep neural networks for question answering.\"},{\"question\":\"What exam and evaluation setup are used?\",\"answer\":\"The system is evaluated on the United States MultiState Bar Examination (MBE), a 200-question multi-choice exam, and performance is also tested under a legal textual entailment evaluation.\"},{\"question\":\"How does the proposed system differ from simpler baselines?\",\"answer\":\"The system achieves good performance without relying on external knowledge and is shown to beat a TFIDF-based baseline.\"}]","Solving Bar Exam Questions with Deep Neural Networks | PDF",1789949564,23]