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Found 10 papers, 3 papers with code
DiffuDetox: A Mixed Diffusion Model for Text Detoxification
Text detoxification is a conditional text generation task aiming to remove offensive content from toxic text.
Bayesian Knowledge-driven Critiquing with Indirect Evidence
This work enables a new paradigm for using rich knowledge content and reasoning over indirect evidence as a mechanism for critiquing interactions with CRS.
LogicRec: Recommendation with Users' Logical Requirements
In this work, we formulate the problem of recommendation with users' logical requirements (LogicRec) and construct benchmark datasets for LogicRec.
FALCON: Faithful Neural Semantic Entailment over ALC Ontologies
Many ontologies, i. e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains, and a lot of them are based on ALC, i. e., a prototypical and expressive DL, or its extensions.
Customized Conversational Recommender Systems
In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context.
TAR: Neural Logical Reasoning across TBox and ABox
Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers.
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Most real-world knowledge graphs (KG) are far from complete and comprehensive.
Description Logic EL++ Embeddings with Intersectional Closure
Since the intersection of boxes remains as a box, the intersectional closure is satisfied.
Personalized Transfer of User Preferences for Cross-domain Recommendation
Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user.
Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning
A mobile app interface usually consists of a set of user interface modules.
