Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

This book is concerned with the classification of multilabeled data and other tasks related to
that subject. The goal of this chapter is to formally introduce the problem, as well as to give a …

Multi-class classification problem is among the most popular and well-studied statistical
frameworks. Modern multi-class datasets can be extremely ambiguous and single-output …

This paper develops the FastRNN and FastGRNN algorithms to address the twin RNN
limitations of inaccurate training and inefficient prediction. Previous approaches have …

State-of-the-art supervised NLP models achieve high accuracy but are also susceptible to
failures on inputs from low-data regimes, such as domains that are not represented in …

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving
hundreds of thousands or even millions of labels. In this paper, we develop a suite of …

The goal in extreme multi-label classification (XMC) is to learn a classifier which can assign
a small subset of relevant labels to an instance from an extremely large set of target labels …

Scalability and accuracy are well recognized challenges in deep extreme multi-label
learning where the objective is to train architectures for automatically annotating a data point …

Extreme multi-label classification (XML) involves tagging a data point with its most relevant
subset of labels from an extremely large label set, with several applications such as product …

We consider the problem of semantic matching in product search: given a customer query,
retrieve all semantically related products from a huge catalog of size 100 million, or more …