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DARTS: Differentiable Architecture Search
This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Expand
XLNet: Generalized Autoregressive Pretraining for Language Understanding
We propose XLNet, a generalized autoregressive pretraining method that leverages the best of both AR language modeling and AE while avoiding their limitations. Expand
A Comparative Study on Feature Selection in Text Categorization
This paper is a comparative study of feature selection methods in statistical learning of text categorization The focus is on aggres sive dimensionality reduction Five meth ods were evaluated including term selection based on document frequency DF informa tion gain IG mutual information MI a test CHI and term strength TS We found IG and CHI most e ective in our ex periments. Expand
RCV1: A New Benchmark Collection for Text Categorization Research
We benchmark several widely used supervised learning methods on RCV1-v2, illustrating the collection's properties, suggesting new directions for research, and providing baseline results for future studies. Expand
A re-examination of text categorization methods
This paper reports a controlled study with statistical signi cance tests on ve text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classi er, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a Naive Bayes (NB) classier. Expand
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. Expand
An Evaluation of Statistical Approaches to Text Categorization
  • Yiming Yang
  • Computer Science
  • Information Retrieval
  • 15 May 1999
This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. Expand
RACE: Large-scale ReAding Comprehension Dataset From Examinations
We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Expand
Topic Detection and Tracking Pilot Study Final Report
Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problemExpand
The Enron Corpus: A New Dataset for Email Classification Research
Automated classification of email messages into user-specific folders and information extraction from chronologically ordered email streams have become interesting areas in text learning. Expand