The emerging trends of multi-label learning
Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …
Attentionxml: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification
Extreme multi-label text classification (XMTC) is an important problem in the era of {\it big
data}, for tagging a given text with the most relevant multiple labels from an extremely large …
data}, for tagging a given text with the most relevant multiple labels from an extremely large …
Fast multi-resolution transformer fine-tuning for extreme multi-label text classification
Extreme multi-label text classification~(XMC) seeks to find relevant labels from an extreme
large label collection for a given text input. Many real-world applications can be formulated …
large label collection for a given text input. Many real-world applications can be formulated …
Lightxml: Transformer with dynamic negative sampling for high-performance extreme multi-label text classification
Extreme multi-label text classification (XMC) is a task for finding the most relevant labels
from a large label set. Nowadays deep learning-based methods have shown significant …
from a large label set. Nowadays deep learning-based methods have shown significant …
Taming pretrained transformers for extreme multi-label text classification
We consider the extreme multi-label text classification (XMC) problem: given an input text,
return the most relevant labels from a large label collection. For example, the input text could …
return the most relevant labels from a large label collection. For example, the input text could …
Deepxml: A deep extreme multi-label learning framework applied to short text documents
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 …
learning where the objective is to train architectures for automatically annotating a data point …
An empirical study on large-scale multi-label text classification including few and zero-shot labels
Large-scale Multi-label Text Classification (LMTC) has a wide range of Natural Language
Processing (NLP) applications and presents interesting challenges. First, not all labels are …
Processing (NLP) applications and presents interesting challenges. First, not all labels are …
XRR: Extreme multi-label text classification with candidate retrieving and deep ranking
Abstract Extreme Multi-label Text Classification (XMTC) is a key task of finding the most
relevant labels from a large label set for a document. Although some deep learning-based …
relevant labels from a large label set for a document. Although some deep learning-based …
Decaf: Deep extreme classification with label features
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 …
subset of labels from an extremely large label set, with several applications such as product …
Extreme multi-label learning for semantic matching in product search
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 …
retrieve all semantically related products from a huge catalog of size 100 million, or more …