View Project

Title:

Neural End-to-End Learning for Computational Argumentation Mining

Abstract:

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.

Comments:

Actions

Suggested Topics

Full Matches (full topic name in abstract)

Partial Matches (at least half of words topic name appear in abstract)

Suggested Resources

Uses abstract to search the content of resources available in Topics. Sorted by relevance.

# Title Author Topic Medium Score
1 Codra: A Novel Discriminative Framework for Rhetorical Analysis Shafiq Joty, Giuseppe Carenini, Raymond T. Ng 999 paper 177.38
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
4 Discriminative Syntax-based Word Ordering for Text Generation Yue Zhang, Stephen Clark 999 paper 163.38
5 DEEP LEARNING FOR CHATBOTS, PART 2 - IMPLEMENTING A RETRIEVAL-BASED MODEL IN TENSORFLOW Denny Britz 445 tutorial 138.59
6 Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience Ismael Rafols, Martin Meyer 999 paper 138.26
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
9 Recent Advances in Document Summarization Jin-ge Yao, Xiaojun Wan, Jianguo Xiao 421 survey 135.67
10 Transfer Learning - Machine Learnings Next Frontier Sebastian Ruder 978 tutorial 133.75
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
13 Gimli: open source and high-performance biomedical name recognition David Campos, Sergio Matos, Jose Oliveira 999 paper 132.04
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
21 The h’-Index, Effectively Improving the h-Index Based on the Citation Distribution Chun-Ting Zhang 999 paper 123.43
22 A Practitioner's Guide to Natural Language Processing (Part I)?—?Processing & Understanding Text Dipanjan (DJ) Sarker 112 resource 122.74
23 Lexicalization and Generative Power in Ccg Marco Kuhlmann, Alexander Koller, Giorgio Satta 999 paper 122.53
24 Natural Language Processing Made Easy – using SpaCy (in Python) Shivam Bansal 131 tutorial 121.91
25 A survey of cross-lingual embedding models Sebastian Ruder 721 tutorial 121.72
26 Lisbon Machine Learning Summer School Highlights Sebastian Ruder 107 resource 121.49
27 Do Altmetrics Work? Twitter and Ten Other Social Web Services Mike Thelwall, Stefanie Haustein, Vincent Larivière, Cassidy R. Sugimoto 999 paper 120.04
28 Multi-Task Learning Objectives for Natural Language Processing Author Unknown 133 resource 118.12
29 A social network's changing statistical properties and the quality of human innovation Brian Uzzi 999 paper 118.07
30 An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship J. E. Hirsch 999 paper 115.27
31 An Intuitive Guide to Linear Algebra Kalid Azad 121 tutorial 114.25
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
34 Rohan & Lenny #3: Recurrent Neural Networks & LSTMs Rohan Kapur 741 tutorial 113.87
35 spaCy: Named Entities Spacy 232 resource 112.72
36 K-Means & Other Clustering Algorithms: A Quick Intro with Python Nikos Koufos 571 tutorial 112.51
37 Negated bio-events: analysis and identification Raheel Nawaz, Paul Thompson, Sophia Ananiadou 999 paper 112.36
38 Natural Language Processing in Artificial Intelligence is almost human-level accurate. Worse yet, it gets smart! Rafal 133 tutorial 110.71
39 Many languages, one parser Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, Noah A Smith 999 paper 110.64
40 Four deep learning trends from ACL 2017: Part 2 Abigail See 713 resource 110.03
41 The history and meaning of the journal impact factor Eugene Garfield 999 paper 109.59
42 Four deep learning trends from ACL 2017: Part 1 Abigail See 713 resource 109.45
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
46 Machine Learning for Humans Vishal Maini, Samer Sabri 134 tutorial 105.92
47 Train Neural Machine Translation Models with Sockeye Felix Hieber, Tobias Domhan 753 tutorial 104.82
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
51 Deep Learning for NLP, advancements and trends in 2017 Javier 711 resource 103.41
52 Automatic Labeling of Semantic Roles Daniel Gildea, Daniel Jurafsky 999 paper 101.97
53 Word embeddings in 2017: Trends and future directions Sebastian Ruder 721 resource 101.90
54 Requests for Research Sebastian Ruder 921 resource 101.58
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
57 Language modeling a billion words Nicholas Leonard 742 tutorial 100.00
58 Some Highlights of MILA Deep Learning and Reinforcement Learning Summer Schools 2017 Mostafa Dehghani 857 resource 99.74
59 Gensim integration with scikit-learn and Keras Chinmaya Pancholi 713 library 99.49
60 Rohan #2: Artificial intelligence, ?Progress/?Time Rohan Kapur 811 tutorial 99.25
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
65 Learning AI if You Suck at Math?—?P7?—?The Magic of Natural Language Processing Daniel Jeffries 133 tutorial 98.16
66 Language Processing Pipelines Author Unknown 731 resource 97.68
67 Python TensorFlow Tutorial – Build a Neural Network Andy Thomas 731 tutorial 97.26
68 An end to end implementation of a Machine Learning pipeline Spandan Madan 107 tutorial 97.22
69 Vector Calculus: Understanding the Dot Product Kalid Azad 101 tutorial 97.21
70 Is science becoming more interdisciplinary? Measuring and mapping six research fields over time Alan L. Porter, Ismael Rafols 999 paper 97.17
71 A comprehensive beginner's guide to create a Time Series Forecast (with Codes in Python) Aarshay Jain 921 resource 97.07
72 Simple Beginner’s guide to Reinforcement Learning & its implementation Faizan Shaikh 713 tutorial 97.01
73 Analyzing the Meaning of Sentences Steven Bird, Ewan Klein, Edward Loper 721 course 96.77
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
75 Yet Another Twitter Sentiment Analysis Part 1?—?tackling class imbalance The Rickest Ricky 381 resource 96.68
76 Introduction to Computational Linguistics and Dependency Trees in data science Shivam Bansal 711 resource 96.17
77 Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences Hongyuan Mei, Mohit Bansal, Matthew R. Walter 999 paper 96.06
78 BiLSTM-CNN-CRF Implementation for Sequence Tagging UKPLab 231 library 96.05
79 Image-to-Image Translation in Tensorflow Christopher Hesse 731 tutorial 95.99
80 Tombones Computer Vision Blog Tomasz Malisiewicz 958 resource 95.94
81 Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs Swabha Swayamdipta, Miguel Ballesteros, Chris Dyer, Noah A Smith 999 paper 95.86
82 40 Interview Questions asked at Startups in Machine Learning / Data Science ANALYTICS VIDHYA CONTENT TEAM 107 tutorial 95.80
83 Awesome Python Vinta 131 resource 95.78
84 Machine Learning Morteza Shahriari Nia 107 tutorial 95.53
85 Fundamentals of Deep Learning - Activation Functions and When to Use Them? Dishashree Gupta 711 resource 95.45
86 The NeuroEvolution of Augmenting Topologies (NEAT) Users Page Author Unknown 999 resource 94.95
87 Building a FAQ Chatbot in Python – The Future of Information Searching Yogesh Kulkarni 232 resource 94.60
88 Capsule Networks and the Limitations of CNNs Soham Chatterjee 744 resource 94.05
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
93 The Essential NLP Guide for data scientists (with codes for top 10 common NLP tasks) Author Unknown 115 resource 93.68
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