Publications

Diversity-Enriched Option-Critic
Anand Kamat
Doina Precup
Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales. The … (voir plus)option-critic framework has been demonstrated to learn temporally extended actions, represented as options, end-to-end in a model-free setting. However, feasibility of option-critic remains limited due to two major challenges, multiple options adopting very similar behavior, or a shrinking set of task relevant options. These occurrences not only void the need for temporal abstraction, they also affect performance. In this paper, we tackle these problems by learning a diverse set of options. We introduce an information-theoretic intrinsic reward, which augments the task reward, as well as a novel termination objective, in order to encourage behavioral diversity in the option set. We show empirically that our proposed method is capable of learning options end-to-end on several discrete and continuous control tasks, outperforms option-critic by a wide margin. Furthermore, we show that our approach sustainably generates robust, reusable, reliable and interpretable options, in contrast to option-critic.
2020-11-03
ArXiv (prépublication)
doi.org
arxiv.org
Effectiveness of quarantine and testing to prevent COVID-19 transmission from arriving travelers
Russell Wa
David L Buckeridge
2020-11-03
medRxiv (prépublication)
doi.org
Explainability and Interpretability: Keys to Deep Medicine
Arash Shaban-Nejad
Martin Michalowski
David L Buckeridge
2020-11-02
Explainable AI in Healthcare and Medicine (publié)
doi.org
A Study of Policy Gradient on a Class of Exactly Solvable Models
Gavin McCracken
Colin Daniels
Rosie Zhao
Anna M. Brandenberger
Prakash Panangaden
Doina Precup
Policy gradient methods are extensively used in reinforcement learning as a way to optimize expected return. In this paper, we explore the e… (voir plus)volution of the policy parameters, for a special class of exactly solvable POMDPs, as a continuous-state Markov chain, whose transition probabilities are determined by the gradient of the distribution of the policy's value. Our approach relies heavily on random walk theory, specifically on affine Weyl groups. We construct a class of novel partially observable environments with controllable exploration difficulty, in which the value distribution, and hence the policy parameter evolution, can be derived analytically. Using these environments, we analyze the probabilistic convergence of policy gradient to different local maxima of the value function. To our knowledge, this is the first approach developed to analytically compute the landscape of policy gradient in POMDPs for a class of such environments, leading to interesting insights into the difficulty of this problem.
2020-11-02
ArXiv (prépublication)
arxiv.org
arxiv.org
ComplexDataLab at WNUT-2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents
Kellin Pelrine
Jacob Danovitch
Albert Orozco Camacho
Reihaneh Rabbany
Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present … (voir plus)Gapformer, which effectively classifies content as informative or not. It reformulates the problem as graph classification, drawing on not only the tweet but connected webpages and entities. We leverage a pre-trained language model as well as the connections between nodes to learn a pooled representation for each document network. We show it outperforms several competitive baselines and present ablation studies supporting the benefit of the linked information. Code is available on Github.
2020-10-31
WNUT (publié)
doi.org
Deconstructing word embedding algorithms
Kian Kenyon-Dean
Edward Newell
Jackie Chi Kit Cheung
Word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications. Uncontextual… (voir plus)ized word embeddings are used in many NLP tasks today, especially in resource-limited settings where high memory capacity and GPUs are not available. Given the historical success of word embeddings in NLP, we propose a retrospective on some of the most well-known word embedding algorithms. In this work, we deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of the common conditions that seem to be required for making performant word embeddings. We believe that the theoretical findings in this paper can provide a basis for more informed development of future models.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
Experience Grounds Language
Yonatan Bisk
Ari Holtzman
Jesse D. Thomason
Jacob Andreas
Yoshua Bengio
Joyce Yue Chai
Mirella Lapata
Angeliki Lazaridou
Jonathan May
Aleksandr Nisnevich
Nicolas Pinto
Joseph Turian
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
Jonathan Pilault
Raymond Li
Sandeep Subramanian
Christopher Pal
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarizati… (voir plus)on. We perform a simple extractive step before generating a summary, which is then used to condition the transformer language model on relevant information before being tasked with generating a summary. We show that this extractive step significantly improves summarization results. We also show that this approach produces more abstractive summaries compared to prior work that employs a copy mechanism while still achieving higher rouge scores. Note: The abstract above was not written by the authors, it was generated by one of the models presented in this paper.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
Factual Error Correction for Abstractive Summarization Models
Meng Cao
Yue Dong
Jiapeng Wu
Jackie Chi Kit Cheung
Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre… (voir plus)-trained with self-supervised methods. However, ensuring the factual consistency of the generated summaries for abstractive summarization systems is a challenge. We propose a post-editing corrector module to address this issue by identifying and correcting factual errors in generated summaries. The neural corrector model is pre-trained on artificial examples that are created by applying a series of heuristic transformations on reference summaries. These transformations are inspired by an error analysis of state-of-the-art summarization model outputs. Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset. We also find that transferring from artificial error correction to downstream settings is still very challenging.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
Inference and Prediction Diverge in Biomedicine
Danilo Bzdok
Denis Engemann
Bertrand Thirion
In the 20th century, many advances in biological knowledge and evidence-based medicine were supported by p values and accompanying methods. … (voir plus)In the early 21st century, ambitions toward precision medicine place a premium on detailed predictions for single individuals. The shift causes tension between traditional regression methods used to infer statistically significant group differences and burgeoning predictive analysis tools suited to forecast an individual's future. Our comparison applies linear models for identifying significant contributing variables and for finding the most predictive variable sets. In systematic data simulations and common medical datasets, we explored how variables identified as significantly relevant and variables identified as predictively relevant can agree or diverge. Across analysis scenarios, even small predictive performances typically coincided with finding underlying significant statistical relationships, but not vice versa. More complete understanding of different ways to define “important” associations is a prerequisite for reproducible research and advances toward personalizing medical care.
2020-10-31
Patterns (publié)
doi.org
Multi-Fact Correction in Abstractive Text Summarization
Yue Dong
Shuohang Wang
Zhe Gan
Yu Cheng
Jackie CK Cheung
Jingjing Liu
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
Yao Lu
Yue Dong
Laurent Charlin
Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-sc… (voir plus)ale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and empirical results—using several state-of-the-art models trained on the Multi-XScience dataset—reveal that Multi-XScience is well suited for abstractive models.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org