Publications

Recursive Top-Down Production for Sentence Generation with Latent Trees
Shawn Tan
Yikang Shen
Timothy J. O'Donnell
Alessandro Sordoni
Aaron Courville
We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic p… (see more)rogramming algorithm that marginalises over latent binary tree structures with
2020-10-31
Findings of the Association for Computational Linguistics: EMNLP 2020 (published)
doi.org
arxiv.org
Supervised Seeded Iterated Learning for Interactive Language Learning
Yuchen Lu
Soumye Singhal
Florian Strub
Olivier Pietquin
Aaron Courville
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (published)
doi.org
arxiv.org
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Jiapeng Wu
Meng Cao
Jackie Chi Kit Cheung
William L. Hamilton
Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this probl… (see more)em by augmenting methods for static knowledge graphs to leverage time-dependent representations. However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions. Additionally, prior work does not explicitly address the temporal sparsity and variability of entity distributions in TKGs. We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques. Experiments on standard TKG tasks show that our approach provides substantial gains compared to the previous state of the art, achieving a 10.7% average relative improvement in Hits@10 across three standard benchmarks. Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (published)
doi.org
arxiv.org
TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization
Clement Jumel
Annie Priyadarshini Louis
Jackie CK Cheung
Human-written texts contain frequent generalizations and semantic aggregation of content. In a document, they may refer to a pair of named e… (see more)ntities such as ‘London’ and ‘Paris’ with different expressions: “the major cities”, “the capital cities” and “two European cities”. Yet generation, especially, abstractive summarization systems have so far focused heavily on paraphrasing and simplifying the source content, to the exclusion of such semantic abstraction capabilities. In this paper, we present a new dataset and task aimed at the semantic aggregation of entities. TESA contains a dataset of 5.3K crowd-sourced entity aggregations of Person, Organization, and Location named entities. The aggregations are document-appropriate, meaning that they are produced by annotators to match the situational context of a given news article from the New York Times. We then build baseline models for generating aggregations given a tuple of entities and document context. We finetune on TESA an encoder-decoder language model and compare it with simpler classification methods based on linguistically informed features. Our quantitative and qualitative evaluations show reasonable performance in making a choice from a given list of expressions, but free-form expressions are understandably harder to generate and evaluate.
2020-10-31
Conference on Empirical Methods in Natural Language Processing (published)
doi.org
You could have said that instead: Improving Chatbots with Natural Language Feedback
Makesh Narsimhan Sreedhar
Kun Ni
Siva Reddy
The ubiquitous nature of dialogue systems and their interaction with users generate an enormous amount of data. Can we improve chatbots usin… (see more)g this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator’s goal is to convert the feedback into a response that answers the user’s previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94%to 75.96% in ranking correct responses on the PERSONACHATdataset, a large improvement given that the original model is already trained on 131k samples.
2020-10-31
Findings of the Association for Computational Linguistics: EMNLP 2020 (published)
doi.org
arxiv.org
Neuroimaging: into the Multiverse
Jessica Dafflon
Pedro F. da Costa
František Váša
Ricardo Pio Monti
Danilo Bzdok
Peter J. Hellyer
Federico Turkheimer
Jonathan Smallwood
Emily J. H. Jones
Robert Leech
For most neuroimaging questions the huge range of possible analytic choices leads to the possibility that conclusions from any single analyt… (see more)ic approach may be misleading. Examples of possible choices include the motion regression approach used and smoothing and threshold factors applied during the processing pipeline. Although it is possible to perform a multiverse analysis that evaluates all possible analytic choices, this can be computationally challenging and repeated sequential analyses on the same data can compromise inferential and predictive power. Here, we establish how active learning on a low-dimensional space that captures the inter-relationships between analysis approaches can be used to efficiently approximate the whole multiverse of analyses. This approach balances the benefits of a multiverse analysis without the accompanying cost to statistical power, computational power and the integrity of inferences. We illustrate this approach with a functional MRI dataset of functional connectivity across adolescence, demonstrating how a multiverse of graph theoretic and simple pre-processing steps can be efficiently navigated using active learning. Our study shows how this approach can identify the subset of analysis techniques (i.e., pipelines) which are best able to predict participants’ ages, as well as allowing the performance of different approaches to be quantified.
2020-10-28
bioRxiv (preprint)
doi.org
NutriQuébec: a unique web-based prospective cohort study to monitor the population’s eating and other lifestyle behaviours in the province of Québec
Annie Lapointe
Catherine Laramée
Ariane Belanger-Gravel
David L Buckeridge
Sophie Desroches
Didier Garriguet
Lise Gauvin
Simone Lemieux
Céline Plante
Benoit Lamarche
2020-10-27
BMJ Open (published)
doi.org
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Iulian Vlad Serban
Chinnadhurai Sankar
Michael Pieper
Joelle Pineau
Yoshua Bengio
Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-… (see more)world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized transition model of the environment with rollout simulations to learn an effective policy from few examples. The learned transition model employs an abstract, discrete (bottleneck) state, which increases sample efficiency by reducing the number of model parameters and by exploiting structural properties of the environment. We provide a mathematical analysis of the Bottleneck Simulator in terms of fixed points of the learned policy, which reveals how performance is affected by four distinct sources of error: an error related to the abstract space structure, an error related to the transition model estimation variance, an error related to the transition model estimation bias, and an error related to the transition model class bias. Finally, we evaluate the Bottleneck Simulator on two natural language processing tasks: a text adventure game and a real-world, complex dialogue response selection task. On both tasks, the Bottleneck Simulator yields excellent performance beating competing approaches.
2020-10-26
Journal of Artificial Intelligence Research (published)
doi.org
arxiv.org
Association between extreme precipitation, drinking water and acute gastrointestinal illness in the Great Lakes
R. Graydon
M. Mezzacapo
J. Boehme
David L Buckeridge
S. Foldy
T. Edge
J. Brubacher
L. Chan
M. Dellinger
E. Faustman
J. Rose
T. Takaro
2020-10-25
ISEE Conference Abstracts (published)
doi.org
DoMoBOT: a bot for automated and interactive domain modelling
Rijul Saini
Gunter Mussbacher
Jin L.C. Guo
Jörg Kienzle
Domain modelling transforms domain problem descriptions written in natural language (NL) into analyzable and concise domain models (class di… (see more)agrams) during requirements analysis or the early stages of design in software development. Since the practice of domain modelling requires time in addition to modelling skills and experience, several approaches have been proposed to automate or semi-automate the construction of domain models from problem descriptions expressed in NL. Despite the existing work on domain model extraction, some significant challenges remain unaddressed: (i) the extracted domain models are not accurate enough to be used directly or with minor modifications in software development, (ii) existing approaches do not facilitate the tracing of the rationale behind the modelling decisions taken by the model extractor, and (iii) existing approaches do not provide interactive interfaces to update the extracted domain models. Therefore, in this paper, we introduce a domain modelling bot called DoMoBOT, explain its architecture, and implement it in the form of a web-based prototype tool. The bot automatically extracts a domain model from a problem description written in NL with an accuracy higher than existing approaches. Furthermore, the bot enables modellers to update a part of the extracted domain model and in response the bot re-configures the other parts of the domain model pro-actively. To improve the accuracy of extracted domain models, we combine the techniques of Natural Language Processing and Machine Learning. Finally, we evaluate the accuracy of the extracted domain models.
2020-10-25
Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (published)
doi.org
Learning Domain Randomization Distributions for Training Robust Locomotion Policies
Melissa Mozian
Juan Camilo Gamboa Higuera
David Meger
Gregory Dudek
This paper considers the problem of learning behaviors in simulation without knowledge of the precise dynamical properties of the target rob… (see more)ot platform(s). In this context, our learning goal is to mutually maximize task efficacy on each environment considered and generalization across the widest possible range of environmental conditions. The physical parameters of the simulator are modified by a component of our technique that learns the Domain Randomization (DR) that is appropriate at each learning epoch to maximally challenge the current behavior policy, without being overly challenging, which can hinder learning progress. This so-called sweet spot distribution is a selection of simulated domains with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution; and 2) The DR distribution made as wide as possible, to increase variability in the environments. These properties aim to ensure the trajectories encountered in the target system are close to those observed during training, as existing methods in machine learning are better suited for interpolation than extrapolation. We show how adapting the DR distribution while training context-conditioned policies results in improvements on jump-start and asymptotic performance when transferring a learned policy to the target environment1.
2020-10-23
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)
doi.org
Veille sur les outils numériques en santé dans le contexte de COVID-19
Aude Motulsky
Philippe Després
Cecile Petitgand
Jean Noel Nikiema
Catherine Régis
Jean-Louis Denis
2020-10-22
(published)
doi.org