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Neural End-to-End Learning for Computational Argumentation Mining


We investigate neural techniques for end-to-end computational argumentation min-ing (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiL-STMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning ‘natural’ subtasks, in a multi-task learning setup, improves performance.



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# Title Author Topic Medium Score
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2 Similarity-driven Semantic Role Induction via Graph Partitioning Joel Lang, Mirella Lapata 999 paper 167.01
3 Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! Sebastian Ruder 641 resource 164.36
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7 Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano Denny Britz 742 tutorial 137.42
8 Recurrent Neural Networks Tutorial, Part 2 - Implementing a RNN with Python, Numpy, and Theano Denny Britz 741 tutorial 136.45
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11 RNNs in Tensorflow, a Practical Guide and Undocumented Features Denny Britz 741 tutorial 133.49
12 Recurrent Neural Networks Tutorial, Part 3- Backpropagation Through Time and Vanishing Gradients Denny Britz 741 tutorial 132.39
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14 Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs Denny Britz 741 tutorial 130.34
15 DEEP LEARNING FOR CHATBOTS, PART 1 - INTRODUCTION Denny Britz 445 tutorial 129.90
16 Deep Learning for NLP Best Practices Sebastian Ruder 713 tutorial 128.06
17 Bayesian Statistics explained to Beginners in Simple English NSS 102 tutorial 127.50
18 An Overview of Multi-Task Learning in Deep Neural Networks Sebastian Ruder 829 tutorial 125.60
19 The data that transformed AI research—and possibly the world Dave Gershgorn 107 resource 125.22
20 Neural Information Retrieval: At the End of the Early Years Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek K 713 resource 124.18
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22 A Practitioner's Guide to Natural Language Processing (Part I)?—?Processing & Understanding Text Dipanjan (DJ) Sarker 112 resource 122.74
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26 Lisbon Machine Learning Summer School Highlights Sebastian Ruder 107 resource 121.49
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32 The Unreasonable Effectiveness of Recurrent Neural Networks Andrej Karpathy 741 survey 114.04
33 spaCy 101: Everything you need to know Author Unknown 731 resource 113.89
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43 Deep Learning from first principles in Python, R and Octave – Part 3 Tinniam V Ganesh 711 resource 109.15
44 State-of-the-art neural coreference resolution for chatbots Thomas Wolf 756 tutorial 107.09
45 A general framework for analysing diversity in science, technology and society Andy Stirling 999 paper 106.84
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48 Written Memories: Understanding, Deriving and Extending the LSTM R2RT 742 resource 104.30
49 An Overview of Proxy-label Approaches for Semi-supervised Learning Sebastian Ruder 581 resource 103.77
50 Word2vec in Python, Part Two: Optimizing Radim Rehurek 721 tutorial 103.63
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55 Neural Networks Tutorial – A Pathway to Deep Learning Andy Thomas 711 tutorial 100.32
56 The Definitive Guide to Natural Language Processing Javier Couto 133 tutorial 100.26
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59 Gensim integration with scikit-learn and Keras Chinmaya Pancholi 713 library 99.49
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61 A Beginner’s Guide to Deep Reinforcement Learning Adam Gibson, Chris Nicholson, Josh Patterson 857 library 99.23
62 Parsing English in 500 Lines of Python Matthew Honnibal 242 tutorial 98.51
63 DeepMind has a bigger plan for its newest Go-playing AI Dave Gershgorn 811 resource 98.46
64 Ultimate Guide to Understand & Implement Natural Language Processing (with codes in Python) Shivam Bansal 131 tutorial 98.40
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71 A comprehensive beginner's guide to create a Time Series Forecast (with Codes in Python) Aarshay Jain 921 resource 97.07
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74 Clustering cliques for graph-based summarization of the biomedical research literature Han Zhang, Marcelo Fiszman, Dongwook Shin, Bartomiej Wilkowski, Thomas Rindflesch 999 paper 96.69
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76 Introduction to Computational Linguistics and Dependency Trees in data science Shivam Bansal 711 resource 96.17
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78 BiLSTM-CNN-CRF Implementation for Sequence Tagging UKPLab 231 library 96.05
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80 Tombones Computer Vision Blog Tomasz Malisiewicz 958 resource 95.94
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87 Building a FAQ Chatbot in Python – The Future of Information Searching Yogesh Kulkarni 232 resource 94.60
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89 A noob's guide to implementing RNN-LSTM using Tensorflow Monik 743 resource 93.97
90 A Crash Course in Python for Scientists Rick Muller 131 tutorial 93.80
91 The Building Blocks of Interpretability Chris Olah 614 resource 93.80
92 How to Escape Saddle Points Efficiently Chi Jin*, Rong Ge, Praneeth Netrapalli , Sham M. Kakade, Michael I. Jordan 187 resource 93.74
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94 Twitter Sentiment Analysis Using Combined LSTM-CNN Models Author Unknown 381 resource 93.65
95 How to think in graphs: An illustrative introduction to Graph Theory and its applications Vardan Grigoryan 967 resource 93.56