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Found 9 papers, 2 papers with code
Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results
In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese.
Clipping Loops for Sample-Efficient Dialogue Policy Optimisation
Second, in advantage clipping, we estimate and clip the advantages of useless responses and normal ones separately.
Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems
In order to improve the sample-efficiency of deep reinforcement learning (DRL), we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS).
Tree-Structured Semantic Encoder with Knowledge Sharing for Domain Adaptation in Natural Language Generation
Domain adaptation in natural language generation (NLG) remains challenging because of the high complexity of input semantics across domains and limited data of a target domain.
Addressing Objects and Their Relations: The Conversational Entity Dialogue Model
Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e. g., relations.
Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling.
Feudal Dialogue Management with Jointly Learned Feature Extractors
Reinforcement learning (RL) is a promising dialogue policy optimisation approach, but traditional RL algorithms fail to scale to large domains.
Interactive Spoken Content Retrieval by Deep Reinforcement Learning
In our previous work, some hand-crafted states estimated from the present retrieval results are used to determine the proper actions.
A Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN) for Unsupervised Discovery of Linguistic Units and Generation of High Quality Features
The Multi-layered Acoustic Tokenizer (MAT) proposed in this work automatically discovers multiple sets of acoustic tokens from the given corpus.
