Publications

Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples
Krtin Kumar
Jackie CK Cheung
Neural abstractive summarizers generate summary texts using a language model conditioned on the input source text, and have recently achieve… (voir plus)d high ROUGE scores on benchmark summarization datasets. We investigate how they achieve this performance with respect to human-written gold-standard abstracts, and whether the systems are able to understand deeper syntactic and semantic structures. We generate a set of contrastive summaries which are perturbed, deficient versions of human-written summaries, and test whether existing neural summarizers score them more highly than the human-written summaries. We analyze their performance on different datasets and find that these systems fail to understand the source text, in a majority of the cases.
2019-05-31
North American Chapter of the Association for Computational Linguistics (publié)
doi.org
Human Motion Prediction Via Pattern Completion in Latent Representation Space
Yi Tian Xu
Yaqiao Li
David Meger
Inspired by ideas in cognitive science, we propose a novel and general approach to solve human motion understanding via pattern completion o… (voir plus)n a learned latent representation space. Our model outperforms current state-of-the-art methods in human motion prediction across a number of tasks, with no customization. To construct a latent representation for time-series of various lengths, we propose a new and generic autoencoder based on sequence-to-sequence learning. While traditional inference strategies find a correlation between an input and an output, we use pattern completion, which views the input as a partial pattern and to predict the best corresponding complete pattern. Our results demonstrate that this approach has advantages when combined with our autoencoder in solving human motion prediction, motion generation and action classification.
2019-05-28
2019 16th Conference on Computer and Robot Vision (CRV) (publié)
doi.org
arxiv.org
Unsupervised Controllable Text Generation with Global Variation Discovery and Disentanglement
Peng Xu
Yanshuai Cao
Jackie CK Cheung
Existing controllable text generation systems rely on annotated attributes, which greatly limits their capabilities and applications. In thi… (voir plus)s work, we make the first successful attempt to use VAEs to achieve controllable text generation without supervision. We do so by decomposing the latent space of the VAE into two parts: one incorporates structural constraints to capture dominant global variations implicitly present in the data, e.g., sentiment or topic; the other is unstructured and is used for the reconstruction of the source sentences. With the enforced structural constraint, the underlying global variations will be discovered and disentangled during the training of the VAE. The structural constraint also provides a natural recipe for mitigating posterior collapse for the structured part, which cannot be fully resolved by the existing techniques. On the task of text style transfer, our unsupervised approach achieves significantly better performance than previous supervised approaches. By showcasing generation with finer-grained control including Cards-Against-Humanity-style topic transitions within a sentence, we demonstrate that our model can perform controlled text generation in a more flexible way than existing methods.
2019-05-27
ArXiv (prépublication)
arxiv.org
arxiv.org
Activity-Based Analysis of Open Source Software Contributors: Roles and Dynamics
Jinghui Cheng
Jin L.C. Guo
Contributors to open source software (OSS) communities assume diverse roles to take different responsibilities. One major limitation of the … (voir plus)current OSS tools and platforms is that they provide a uniform user interface regardless of the activities performed by the various types of contributors. This paper serves as a non-trivial first step towards resolving this challenge by demonstrating a methodology and establishing knowledge to understand how the contributors' roles and their dynamics, reflected in the activities contributors perform, are exhibited in OSS communities. Based on an analysis of user action data from 29 GitHub projects, we extracted six activities that distinguished four Active roles and five Supporting roles of OSS contributors, as well as patterns in role changes. Through the lens of the Activity Theory, these findings provided rich design guidelines for OSS tools to support diverse contributor roles.
2019-05-26
2019 IEEE/ACM 12th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE) (publié)
doi.org
arxiv.org
Singular Value Automata and Approximate Minimization
Borja Balle
Prakash Panangaden
Doina Precup
The present paper uses spectral theory of linear operators to construct approximately minimal realizations of weighted languages. Our new co… (voir plus)ntributions are: (i) a new algorithm for the SVD decomposition of infinite Hankel matrices based on their representation in terms of weighted automata, (ii) a new canonical form for weighted automata arising from the SVD of its corresponding Hankel matrix and (iii) an algorithm to construct approximate minimizations of given weighted automata by truncating the canonical form. We give bounds on the quality of our approximation.
2019-05-26
Mathematical Structures in Computer Science (publié)
doi.org
arxiv.org
Analysis and Detection of Information Types of Open Source Software Issue Discussions
Deeksha M. Arya
Wenting Wang
Jin L.C. Guo
Jinghui Cheng
Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these co… (voir plus)mments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.
2019-05-24
2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) (publié)
doi.org
arxiv.org
Fairwashing: the risk of rationalization
Ulrich Aivodji
Hiromi Arai
Olivier Fortineau
Sébastien Gambs
Satoshi Hara
Alain Tapp
Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and general… (voir plus)ly complex -- produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques can be beneficial by providing interpretability, they can be used in a negative manner to perform fairwashing, which we define as promoting the false perception that a machine learning model respects some ethical values. In particular, we demonstrate that it is possible to systematically rationalize decisions taken by an unfair black-box model using the model explanation as well as the outcome explanation approaches with a given fairness metric. Our solution, LaundryML, is based on a regularized rule list enumeration algorithm whose objective is to search for fair rule lists approximating an unfair black-box model. We empirically evaluate our rationalization technique on black-box models trained on real-world datasets and show that one can obtain rule lists with high fidelity to the black-box model while being considerably less unfair at the same time.
2019-05-23
Proceedings of the 36th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
GeoMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
Edward J. Smith
Scott Fujimoto
Adriana Romero
David Meger
Mesh models are a promising approach for encoding the structure of 3D objects. Current mesh reconstruction systems predict uniformly distrib… (voir plus)uted vertex locations of a predetermined graph through a series of graph convolutions, leading to compromises with respect to performance or resolution. In this paper, we argue that the graph representation of geometric objects allows for additional structure, which should be leveraged for enhanced reconstruction. Thus, we propose a system which properly benefits from the advantages of the geometric structure of graph encoded objects by introducing (1) a graph convolutional update preserving vertex information; (2) an adaptive splitting heuristic allowing detail to emerge; and (3) a training objective operating both on the local surfaces defined by vertices as well as the global structure defined by the mesh. Our proposed method is evaluated on the task of 3D object reconstruction from images with the ShapeNet dataset, where we demonstrate state of the art performance, both visually and numerically, while having far smaller space requirements by generating adaptive meshes
2019-05-23
Proceedings of the 36th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
GMNN: Graph Markov Neural Networks
Meng Qu
Yoshua Bengio
Jian Tang
This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The… (voir plus) problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutional networks). Statistical relational learning methods can effectively model the dependency of object labels through conditional random fields for collective classification, whereas graph neural networks learn effective object representations for classification through end-to-end training. In this paper, we propose the Graph Markov Neural Network (GMNN) that combines the advantages of both worlds. A GMNN models the joint distribution of object labels with a conditional random field, which can be effectively trained with the variational EM algorithm. In the E-step, one graph neural network learns effective object representations for approximating the posterior distributions of object labels. In the M-step, another graph neural network is used to model the local label dependency. Experiments on object classification, link classification, and unsupervised node representation learning show that GMNN achieves state-of-the-art results.
2019-05-23
Proceedings of the 36th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto
David Meger
Doina Precup
Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, … (voir plus)without offering further possibility for data collection. In this paper, we demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are incapable of learning with data uncorrelated to the distribution under the current policy, making them ineffective for this fixed batch setting. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space in order to force the agent towards behaving close to on-policy with respect to a subset of the given data. We present the first continuous control deep reinforcement learning algorithm which can learn effectively from arbitrary, fixed batch data, and empirically demonstrate the quality of its behavior in several tasks.
2019-05-23
Proceedings of the 36th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Per-Decision Option Discounting
Anna Harutyunyan
Peter Vrancx
Philippe Hamel
Ann Nowé
Doina Precup
In order to solve complex problems an agent must be able to reason over a sufficiently long horizon. Temporal abstraction, commonly modeled … (voir plus)through options, offers the ability to reason at many timescales, but the horizon length is still determined by the discount factor of the underlying Markov Decision Process. We propose a modification to the options framework that naturally scales the agent’s horizon with option length. We show that the proposed option-step discount controls a bias-variance trade-off, with larger discounts (counter-intuitively) leading to less estimation variance.
2019-05-23
Proceedings of the 36th International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb
Jonathan Binas
Anirudh Goyal
Sandeep Subramanian
Ioannis Mitliagkas
Denis Kazakov
Yoshua Bengio
Michael Mozer
Machine learning promises methods that generalize well from finite labeled data. However, the brittleness of existing neural net approaches … (voir plus)is revealed by notable failures, such as the existence of adversarial examples that are misclassified despite being nearly identical to a training example, or the inability of recurrent sequence-processing nets to stay on track without teacher forcing. We introduce a method, which we refer to as \emph{state reification}, that involves modeling the distribution of hidden states over the training data and then projecting hidden states observed during testing toward this distribution. Our intuition is that if the network can remain in a familiar manifold of hidden space, subsequent layers of the net should be well trained to respond appropriately. We show that this state-reification method helps neural nets to generalize better, especially when labeled data are sparse, and also helps overcome the challenge of achieving robust generalization with adversarial training.
2019-05-23
Proceedings of the 36th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press