Publications

BanditSum: Extractive Summarization as a Contextual Bandit

Yue Dong

Yikang Shen

Eric Crawford

Herke van Hoof

Jackie Cheung

In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristical… (see more)ly-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BanditSum is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BanditSum performs significantly better than competing approaches when good summary sentences appear late in the source document.

2018-10-01

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (published)

A Knowledge Hunting Framework for Common Sense Reasoning

Ali Emami

Noelia De La Cruz

Adam Trischler

Kaheer Suleman

Jackie Cheung

We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning tas… (see more)k that requires diverse, complex forms of inference and knowledge. Our method uses a knowledge hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable.

2018-10-01

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (published)

Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation

Tanya Nair

Doina Precup

Douglas Arnold

2018-09-26

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (published)

CNN Prediction of Future Disease Activity for Multiple Sclerosis Patients from Baseline MRI and Lesion Labels

Nazanin Mohammadi Sepahvand

Tal Hassner

Douglas Arnold

2018-09-16

BrainLes@MICCAI (published)

3D U-Net for Brain Tumour Segmentation

Raghav Mehta

2018-09-16

BrainLes@MICCAI (published)

How to Exploit Weaknesses in Biomedical Challenge Design and Organization

Annika Reinke

Matthias Eisenmann

Sinan Onogur

Marko Stankovic

Patrick Scholz

Peter M. Full

Hrvoje Bogunovic

Bennett Landman

Oskar Maier

Bjoern Menze

Gregory C. Sharp

Korsuk Sirinukunwattana

Stefanie Speidel

F. V. D. Sommen

Guoyan Zheng

Henning Müller

Michal Kozubek

Andrew P. Bradley

Pierre Jannin … (see 2 more)

Annette Kopp-Schneider

Lena Maier-Hein

2018-09-13

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (published)

RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours

Raghav Mehta

2018-09-12

Simulation and Synthesis in Medical Imaging (published)

Social-Affiliation Networks: Patterns and the SOAR Model

Dhivya Eswaran

Reihaneh Rabbany

Artur Dubrawski

Christos Faloutsos

2018-09-10

ECML/PKDD (published)

Structured deep Fisher pruning for efficient facial trait classification

Qing Tian

James J. Clark

2018-09-01

Image and Vision Computing (published)

Domain Knowledge Discovery Guided by Software Trace Links

Jin Guo

Natawut Monaikul

Jane Cleland-Huang

Software-intensive projects are specified and modeled using domain terminology. Knowledge of the domain terminology is necessary for perform… (see more)ing many Software Engineering tasks such as impact analysis, compliance verification, and safety certification. However, discovering domain terminology and reasoning about their interrelationships for highly technical software and system engineering domains is a complex task which requires significant domain expertise and human effort. In this paper, we present a novel approach for leveraging trace links in software intensive systems to guide the process of mining facts that contain domain knowledge. The trace links which drive our mining process, define relationships between artifacts such as regulations and requirements and enable a guided search through high-yield combinations of domain terms. Our proof-of-concept evaluation shows that our approach aids in the discovery of domain facts even in highly complex technical domains. These domain facts can provide support for a variety of Software Engineering activities. As a use case, we demonstrate how the mined facts can facilitate the task of project Q&A.

2018-08-21

2018 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE) (published)

The Deconfounded Recommender: A Causal Inference Approach to Recommendation

Yixin Wang

Dawen Liang

Laurent Charlin

David Blei

The goal of a recommender system is to show its users items that they will like. In forming its prediction, the recommender system tries to … (see more)answer: "what would the rating be if we 'forced' the user to watch the movie?" This is a question about an intervention in the world, a causal question, and so traditional recommender systems are doing causal inference from observational data. This paper develops a causal inference approach to recommendation. Traditional recommenders are likely biased by unobserved confounders, variables that affect both the "treatment assignments" (which movies the users watch) and the "outcomes" (how they rate them). We develop the deconfounded recommender, a strategy to leverage classical recommendation models for causal predictions. The deconfounded recommender uses Poisson factorization on which movies users watched to infer latent confounders in the data; it then augments common recommendation models to correct for potential confounding bias. The deconfounded recommender improves recommendation and it enjoys stable performance against interventions on test sets.

2018-08-20

ArXiv (preprint)