[ 
  
  
    
      
      {
      "title": "Build a PC Workshop",
      "url": "https://resume.marzoog.co/opensource/build-pc/",
      "body": "Workshop images:\n\n\n\n\n\n"
      }
    ,
    
    
      
      {
      "title": "CS1 & CS2 Helpdesk",
      "url": "https://resume.marzoog.co/opensource/helpdesk/",
      "body": "Workshop images:\n\n\n"
      }
    ,
    
    
      
      {
      "title": "The Hub",
      "url": "https://resume.marzoog.co/opensource/the-hub/",
      "body": "Event images:\n\n\n\n\n\n\n\n\n"
      }
    
    ,
    
      
      {
      "title": "Creating a Speaking Engagement Index for Speech Therapy Assessment using Deep Learning",
      "url": "https://resume.marzoog.co/projects/therapy-assessment/",
      "body": "Intro\nUniversity of North Texas — Creating a Speaking Engagement Index for Speech Therapy Assessment using Deep Learning: \n\nWorked with a team of six to implement an autoencoder to analyze the effect of dimension reduction on a machine learning model.\nImplemented Google Speech API to an Android application.\nThe research aims to help medical professionals diagnose patients with autism.\n\nProgram Posters\n\nWhat's after the program\n\n"
      }
    ,
    
    
      
      {
      "title": "Android University Application",
      "url": "https://resume.marzoog.co/projects/android-university-application/",
      "body": "Android University Application\nLed a Team of 6 Computer Science and Computer Engineering students to design and implement a university application for Android. The app allowed the users to sign in, view courses list and detail, and see the user profile.\n"
      }
    
    ,
    
      {
      "title": "HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser",
      "url": "https://resume.marzoog.co/publications/hit-scir-at-mrp-2020-transition-based-parser-and-iterative-inference-parser/",
      "body": "This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing. \nThe task includes five frameworks for graph-based meaning representations, i.e., UCCA, EDS, PTG, AMR, and DRG. \nOur solution consists of two sub-systems: \n\ntransition-based parser for Flavor (1) frameworks (UCCA, EDS, PTG)\niterative inference parser for Flavor (2) frameworks (DRG, AMR). \n\nIn the final evaluation, our system is ranked 3rd among the seven team both in Cross-Framework Track and Cross-Lingual Track, with the macro-averaged MRP F1 score of 0.81/0.69.\n"
      }
    ,
    
    
      {
      "title": "N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models",
      "url": "https://resume.marzoog.co/publications/n-ltp-a-open-source-neural-chinese-language-technology-platform-with-pretrained-models/",
      "body": "An open-source neural language technology platform supporting six fundamental Chinese NLP tasks: \n\nlexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition)\nsyntactic parsing (dependency parsing)\nsemantic parsing (semantic dependency parsing and semantic role labeling). \n\nUnlike the existing state-of-the-art toolkits, such as Stanza, that adopt an independent model for each task, N-LTP adopts the multi-task framework by using a shared pre-trained model, which has the advantage of capturing the shared knowledge across relevant Chinese tasks. \nIn addition, knowledge distillation where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher.\nFinally, we provide a collection of easy-to-use APIs and a visualization tool to make users easier to use and view the processing results directly. To the best of our knowledge, this is the first toolkit to support six Chinese NLP fundamental tasks. \n"
      }
    
    ]