Philip Bachman

Towards Information-Seeking Agents

2016 · arXiv (Cornell University)

We develop a general problem setting for training and testing the ability of agents to gather information efficiently. Specifically, we present a collection of tasks in which success requires searching through a partially-observed environment, for …

Data-Efficient Reinforcement Learning with Self-Predictive Representations

2020 · arXiv (Cornell University)

L'efficacité des données reste un défi majeur dans l'apprentissage par renforcement profond. Bien que les techniques modernes soient capables d'atteindre des performances élevées dans des tâches extrêmement complexes, y compris les jeux de stratégie comme …

NewsQA: A Machine Comprehension Dataset

2017

We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans …

Machine Comprehension by Text-to-Text Neural Question Generation

2017

Xingdi Yuan, Tong Wang, Caglar Gulcehre, Alessandro Sordoni, Philip Bachman, Saizheng Zhang, Sandeep Subramanian, Adam Trischler. Proceedings of the 2nd Workshop on Representation Learning for NLP. 2017.

NewsQA: A Machine Comprehension Dataset

2016 · arXiv (Cornell University)

We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans …

Assessing Factoid Question-Answer Generation for Portuguese (Short Paper)

2017 · arXiv (Cornell University)

We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard …