Laurent Charlin

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

2017 · arXiv (Cornell University)

A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation. This makes BPTT both …

Towards Deep Conversational Recommendations

2018 · PolyPublie (École Polytechnique de Montréal)

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated …

Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations

2021

Explainability of machine learning models has gained considerable attention within our research community given the importance of deploying more reliable machine-learning systems. Explanability can also be helpful for model debugging. In computer vision applications, most …

Modeling User Exposure in Recommendation

2016

Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feedback settings, all the items, including the ones that a user did not …

A Survey of Available Corpora for Building Data-Driven Dialogue Systems

2015 · arXiv (Cornell University)

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical …

How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

2016 · arXiv (Cornell University)

We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available. Recent works in response generation have adopted metrics from machine translation to compare a model's generated …

Factorization Meets the Item Embedding

2016

Matrix factorization (MF) models and their extensions are standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose a co-factorization model, …

Session-Based Social Recommendation via Dynamic Graph Attention Networks

2019

Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that …

Language GANs Falling Short

2020 · International Conference on Learning Representations

Traditional natural language generation (NLG) models are trained using maximum likelihood estimation (MLE) which differs from the sample generation inference procedure. During training the ground truth tokens are passed to the model, however, during inference, …