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Found 11 papers, 3 papers with code
Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network
We introduce a framework of Monte Carlo Tree Search with Double-q Dueling network (MCTS-DDU) for task-completion dialogue policy learning.
Model-based Reinforcement Learning
reinforcement-learning
+2
Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing
To further reduce storage costs, inspired by task arithmetic sparsity, we decouple multiple fine-tuned experts into a dense expert and several sparse experts.
KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph
Entity synonyms discovery is crucial for entity-leveraging applications.
Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model
In addition, it reduces human involvement in template design by using a text stitch model to automatically stitch adjacent template units, which is a step that usually requires careful template design and limits template reusability.
Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pair-wise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model.
A User-Centered Concept Mining System for Query and Document Understanding at Tencent
We further present our techniques to tag documents with user-centered concepts and to construct a topic-concept-instance taxonomy, which has helped to improve search as well as news feeds recommendation in Tencent QQ Browser.
Multiresolution Graph Attention Networks for Relevance Matching
In this paper, we are especially interested in relevance matching between a piece of short text and a long document, which is critical to problems like query-document matching in information retrieval and web searching.
Learning to Generate Questions by Learning What not to Generate
In CGC-QG, we design a multi-task labeling strategy to identify whether a question word should be copied from the input passage or be generated instead, guiding the model to learn the accurate boundaries between copying and generation.
Matching Natural Language Sentences with Hierarchical Sentence Factorization
The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies.
Growing Story Forest Online from Massive Breaking News
We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures in an online fashion.
Ranked #3 on
Information Threading
on NewSHead
Matching Article Pairs with Graphical Decomposition and Convolutions
Identifying the relationship between two articles, e. g., whether two articles published from different sources describe the same breaking news, is critical to many document understanding tasks.
