Argumentor - a Cognitive Advisor for Young Attorneys


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Boaz Carmelli's presentation at the Cognitive Systems Institute Group Speaker Series on December 10, 2015. Boaz is a Research Scientist at IBM Research in Haifa.

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Argumentor - a Cognitive Advisor for Young Attorneys

  1. 1. A new legal aid. Advisor and mentor for young attorneys. IBM Argumentor Powered by the Debater Technology Boaz Carmeli, IBM Research – Haifa
  2. 2. Agenda – things that I am intending to cover Short introduction and background: the Debater grand challenge Our technology focus: Pro-con analysis via Machine learning (deep learning) and NLP The Argumentor application: Pro-con analysis at the legal domain Argumentor: Short demo The university relations aspects: legal experts for requirements definition and legal text annotation Sumary and questions….
  3. 3. Background – The debater grand challenge The Debater Grand Challenge aims on developing technology that assist humans to debate and reason... The Debater vision is that of an intelligent system able to take raw information and digest and reason on that information, to understand the context, and to construct arguments pro and con any subject… The Debater uses complex analytic pipeline a.k.a Argument Construction Engine (ACE) 3
  4. 4. Pro/Con Analysis Goal: – given two related sentences e.g., A claim and an evidence or, A thesis and a fact – determine for each pair: Whether the second supports the first (PRO) or contests it (CON) Example – Claim: An evidence obtained from a search without warrant cannot be used in court – Evidence: “The Fourth Amendment provides that "the right of the people to be secure in their persons, houses, papers, and effects, against unreasonable searches and seizures, shall not be violated.” (PRO) “It is frequently argued that in dealing with the rapidly unfolding and often dangerous situations on city streets the police are in need of an escalating set of flexible responses, graduated in relation to the amount of information they possess.“ (CON)
  5. 5. Deep Learning for Pro-Con Analysis Deep learning is a promising machine learning subfield that gains huge momentum lately – Beats state-of-the-art algorithms in areas such as computer vison, speech recognition and natural language processing Sentence one Sentence two Deep learning 2 i.e., Recurrent Neural Net (RNN) Deep learning 1 i.e., Recurrent Neural Net (RNN) Deep Learning layer 3 i.e., Classification Pro-con results
  6. 6. Argumentor’s Target Professional Young attorney, less than 5 years of experience Usually takes a role of a Junior associate in mid-large law firms Chosen based on a market research by Watson group: 6
  7. 7. Argumentor Demo
  8. 8. Web client Supreme court decisions 1937-1975 (FLITE) Keyword and Concept extraction Annotation tool Annotation Argument detection Debater claim detection service ActiveLearning via Exemplar Clustering Annotation management Alchemy Langage NLClassifier Argument clustering Argument classification Solution Architecture 8 IBM Argumentor
  9. 9. Cognitive Elements Argument Construction – Deep natural language processing - Argumentor processes legal cases for detecting arguments, based on technology from the Debater grand challenge Natural language input – Argumentor receives a short case brief as its input Interactivity – Argumentor integrates in current workflow of the legal professional Processes email as input Allows human feedback at each step Learns argument classification based on several examples from the user Helps the user focus on what is of her interest by interactive highlighting Supervised Machine Learning – For this hackathon, we annotated sample data of real legal cases to teach the computer to classify and evaluate arguments 9
  10. 10. University Relations Aspects Won a “Legal Argumentation Structuring and Gamification” UR country project Gave two lectures at the “Institute for Legal Implications of Emerging Technologies” program at Interdisciplinary Center, (IDC) Herzliya, Israel Instructing a group of 4 students on ‘Legal and AI’ project – Legal csaes annotation guidelines – User requirements Search collaboration with Robert-Jan Sips and the Netherlands academy – Application for the IBM University Programs to fund a PhD fellowship for crowdsourcing and nichsourcing research in the legal domain – Investigating annotation platforms such as Crowdflower and Watson Knowledge Studio (WKS)
  11. 11. Summary Debater provides leading technology for argument construction Argumentor is a cognitive application that assists young attorney in her legal research tasks – Based on argument construction pipeline, namely claim detection and pro-con analysis Collaboration with law schools and university provides a jump-start into this complex domain
  12. 12. Hackathon Team Thanks to: IBM Research - Haifa •Machine Learning Technologies group •Medical Imaging Analytics Watson Emerging Products Design Watson Implementations 12
  13. 13. Backup
  14. 14. Backoffice work Downloaded 7,500 US Supreme Court cases from 1937-1975 Used ACELab and GATE to annotate legal data – Annotated by our legal professional on board – a lawyer team member from Watson Implementations Created three exemplary use cases Conducted search for relevant cases for all three cases Annotated arguments from Supreme Court cases – Created guidelines for annotation of legal argument Trained NLClassifier based on winning and losing arguments Downloaded structured data of US Supreme court cases Preprocessed all 7,500 cases by running the Debater pipeline offline due to performance considerations 14
  15. 15. IBM Argumentor Flow - Input Attorney pastes an email or brief that describes his case Argumentor uses AlchemyAPI to extract keywords and concepts Argumentor reranks and filters results to keep only relevant legal concepts Attorney can add and remove keywords 15
  16. 16. IBM Argumentor Flow – Argument Construction Argumentor uses Debater’s ACE to construct arguments Argumentor augments arguments data with case data Attorney can look at the relevant cases Argumentor found Attorney can focus on arguments using facets based on structured data 16
  17. 17. IBM Argumentor Flow – global cases view Argumentor presents relevant cases taxonomy in a treemap Argumentor presents wining and losing parties statistics Attorney can interact with views so Argumentor will focus on certain arguments 17
  18. 18. IBM Argumentor Flow – cases view Argumentor presents relevant color coded cases in a list Argumentor provides a case view in a glance: – Case relevancy – Case characteristics – Case taxonomy Attorney can interact with views so Argumentor will focus on arguments from a specific case 18
  19. 19. IBM Argumentor Flow – arguments pro/con analysis Argumentor presents arguments sorted by relevancy Argumentor provides a quick link between an argument and its case Attorney can place a few arguments in folders - arguments that support his client or against him. Argumentor learns to classify the rest of the arguments automatically 19
  20. 20. ing 20