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Found 12 papers, 4 papers with code
Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations
We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style.
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems
Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively.
MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration
In previous work, researchers have developed several calibration methods to post-process the outputs of a predictor to obtain calibrated values, such as binning and scaling methods.
Computation Resource Allocation Solution in Recommender Systems
On the other hand, the response time of these systems is strictly limited to a short period, e. g. 300 milliseconds in our real system, which is also being exhausted by the increasingly complex models and algorithms.
Formality Style Transfer with Shared Latent Space
Conventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training.
Global Public Health Surveillance using Media Reports: Redesigning GPHIN
Global public health surveillance relies on reporting structures and transmission of trustworthy health reports.
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer
Formality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education.
Real-time Change Point Detection using On-line Topic Models
Detecting changes within an unfolding event in real time from news articles or social media enables to react promptly to serious issues in public safety, public health or natural disasters.
Response Generation by Context-aware Prototype Editing
Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses.
Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags
Detecting events from social media data has important applications in public security, political issues, and public health.
Extracting Discriminative Keyphrases with Learned Semantic Hierarchies
We show that such knowledge can be used to construct better discriminative keyphrase extraction systems that do not assume a static, fixed set of keyphrases for a document.
