View Project

Title:

Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification

Abstract:

Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation frame-work in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.

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 128.25
2 Similarity-driven Semantic Role Induction via Graph Partitioning Joel Lang, Mirella Lapata 999 paper 106.86
3 NLP’s generalization problem, and how researchers are tackling it Ana Marasovic 711 resource 98.22
4 Negated bio-events: analysis and identification Raheel Nawaz, Paul Thompson, Sophia Ananiadou 999 paper 96.93
5 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 232 survey 96.22
6 An Overview of Multi-Task Learning in Deep Neural Networks Sebastian Ruder 829 tutorial 94.74
7 Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! Sebastian Ruder 641 resource 94.06
8 State-of-the-art neural coreference resolution for chatbots Thomas Wolf 756 tutorial 93.21
9 NLP's ImageNet moment has arrived Sebastian Ruder 862 resource 92.02
10 Word embeddings in 2017: Trends and future directions Sebastian Ruder 721 resource 86.56
11 Generative Models Andrej Karpathy, Pieter Abbeel, Greg Brockman, Peter Chen, Vicki Cheung, Rocky Duan, Ian Goodfellow, Durk Kingma, Jonathan Ho, Rein Houthooft, Tim Salimans, John Schulman, Ilya Sutskever, Wojciech Zar 756 resource 85.42
12 End-to-end speech Anthony Ndirango 863 resource 84.25
13 The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) Adit Deshpande 744 tutorial 83.97
14 Some Highlights of MILA Deep Learning and Reinforcement Learning Summer Schools 2017 Mostafa Dehghani 857 resource 81.07
15 Deconstruction with Discrete Embeddings R2RT 711 resource 80.70
16 The data that transformed AI research—and possibly the world Dave Gershgorn 107 resource 80.55
17 ACL 2017 Report Yuta Kikuchi, Sosuke Kobayashi 711 resource 80.26
18 A Dozen Times Artificial Intelligence Startled the World Sumeet Agrawal 811 resource 80.12
19 Multi-Task Learning Objectives for Natural Language Processing Author Unknown 133 resource 79.65
20 Recent Advances in Document Summarization Jin-ge Yao, Xiaojun Wan, Jianguo Xiao 421 survey 78.39
21 Analyzing the Meaning of Sentences Steven Bird, Ewan Klein, Edward Loper 721 course 77.63
22 Automatic feature engineering using Generative Adversarial Networks Hamaad Shah 711 resource 76.66
23 Variational Inference using Implicit Models, Part I: Bayesian Logistic Regression Ferenc Huszár 364 resource 76.23
24 Learning about the world through video Moritz Mueller-Freitag 811 resource 76.16
25 Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience Ismael Rafols, Martin Meyer 999 paper 75.39
26 Gimli: open source and high-performance biomedical name recognition David Campos, Sergio Matos, Jose Oliveira 999 paper 75.32
27 A general framework for analysing diversity in science, technology and society Andy Stirling 999 paper 74.84
28 Learning when to skim and when to read Alexander Rosenberg Johansen, Bryan McCann, James Bradbury, Richard Socher 713 tutorial 74.30
29 Recommendation in Industry Xavier Amatriain 999 tutorial 74.18
30 Rohan #2: Artificial intelligence, ?Progress/?Time Rohan Kapur 811 tutorial 73.71
31 Fueling the Gold Rush: The Greatest Public Datasets for AI Luke de Oliveira 107 resource 73.67
32 Summaries and notes on Deep Learning research papers Denny Britz 713 resource 73.46
33 Deep Learning Achievements Over the Past Year Eduard Tyantov 711 resource 73.23
34 Lisbon Machine Learning Summer School Highlights Sebastian Ruder 107 resource 72.98
35 New wave of deep neural networks Alfredo Canziani, Abishek Chaurasia, Eugenio Culurciello 713 tutorial 72.79
36 Transfer Learning - Machine Learnings Next Frontier Sebastian Ruder 978 tutorial 72.60
37 Using Artificial Intelligence to Augment Human Intelligence Shan Carter, Michael Nielsen 811 resource 72.46
38 Must Know Tips/Tricks in Deep Neural Networks Xiu-Shen Wei 713 tutorial 72.08
39 A new kind of deep neural networks Eugenio Culurciello 711 resource 71.86
40 Tombones Computer Vision Blog Tomasz Malisiewicz 958 resource 71.01
41 [ICLR][NVIDIA] Progressive generative adversarial networks (GANs) explained with art forgery?—?Part I. Brendan Whitaker 713 resource 70.89
42 Four deep learning trends from ACL 2017: Part 1 Abigail See 713 resource 70.01
43 Text to Video Generation Antonia Antonova 713 resource 70.00
44 From GAN to WGAN Lilian Weng 711 resource 69.93
45 Recurrent Neural Networks Stephen Grossberg 741 paper 69.70
46 Deep Learning in NLP Vered Shwartz 711 resource 69.68
47 On word embeddings - Part 2: Approximating the Softmax Sebastian Ruder 721 tutorial 69.34
48 Parsing English in 500 Lines of Python Matthew Honnibal 242 tutorial 69.24
49 Revisiting Deep Learning as a Non-Equilibrium Process Carlos E. Perez 711 resource 69.13
50 Machine Learning for Humans Vishal Maini, Samer Sabri 134 tutorial 68.79
51 ResNet, AlexNet, VGG, Inception: Understanding various architectures of Convolutional Networks Koustubh 744 resource 68.39
52 FigureQA: an annotated figure dataset for visual reasoning Author Unknown 862 resource 68.10
53 Deep Learning for NLP: An Overview of Recent Trends Elvis 711 resource 67.87
54 Understanding and Implementing CycleGAN in TensorFlow Hardik Bansal, Archit Rathore 731 tutorial 67.83
55 GANs from Scratch 1: A deep introduction. With code in PyTorch and TensorFlow Diego Gomez Mosquera 731 resource 67.42
56 CoNLL-2015 Shared Task: Shallow Discourse Parsing Te Rutherford 242 resource 67.12
57 Towards data set augmentation with GANs Pedro Ferreira 713 resource 66.83
58 Ideas on interpreting machine learning Patrick Hall, Wen Phan, SriSatish Ambati 134 tutorial 66.79
59 Brief History of Machine Learning Eren Golge 107 tutorial 66.69
60 A social network's changing statistical properties and the quality of human innovation Brian Uzzi 999 paper 66.64
61 Convolutional neural networks, Part 1 Adrian Colyer 744 resource 66.50
62 Uncovering the Intuition behind Capsule Networks and Inverse Graphics: Part I Tanay Kothari 641 resource 65.91
63 Uncovering the Intuition behind Capsule Networks and Inverse Graphics Tanay Kothari 711 resource 65.89
64 How do we capture structure in relational data? Matthew Das Sarma 711 resource 65.87
65 Deep Learning from first principles in Python, R and Octave – Part 3 Tinniam V Ganesh 711 resource 65.85
66 Ensemble Learning to Improve Machine Learning Results Vadim Smolyakov 999 resource 65.70
67 Rohan & Lenny #3: Recurrent Neural Networks & LSTMs Rohan Kapur 741 tutorial 65.68
68 Natural Language Processing in Artificial Intelligence is almost human-level accurate. Worse yet, it gets smart! Rafal 133 tutorial 65.55
69 ICML+ACL’18: Structure Back in Play, Translation Wants More Context Andre Martins 956 resource 65.36
70 A survey of transfer learning Karl Weiss, Taghi M. Khoshgoftaar and DingDing Wang 978 resource 65.18
71 An Overview of Proxy-label Approaches for Semi-supervised Learning Sebastian Ruder 581 resource 65.13
72 An On-device Deep Neural Network for Face Detection Computer Vision Machine Learning Team 862 resource 65.06
73 Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs Denny Britz 741 tutorial 65.05
74 Clustering cliques for graph-based summarization of the biomedical research literature Han Zhang, Marcelo Fiszman, Dongwook Shin, Bartomiej Wilkowski, Thomas Rindflesch 999 paper 64.97
75 Rohan & Lenny #2: Convolutional Neural Networks Lenny Khazan 744 tutorial 64.94
76 Open Machine Learning Course. Topic 5. Bagging and Random Forest Yury Kashnitskiy 711 resource 64.91