Search Results for author:
Found 15 papers, 4 papers with code
Generative Dense Retrieval: Memory Can Be a Burden
Memorizing-free matching mechanism from Dense Retrieval (DR) is then introduced to conduct fine-grained intra-cluster matching from clusters to relevant documents.
BatchEval: Towards Human-like Text Evaluation
Significant progress has been made in automatic text evaluation with the introduction of large language models (LLMs) as evaluators.
Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data
In this work, we illustrate the merit of negative data and propose a model specialization framework to distill LLMs with negative samples besides positive ones.
Heterogeneous-Branch Collaborative Learning for Dialogue Generation
Collaborative learning, also known as online knowledge distillation, is an effective way to conduct one-stage group distillation in the absence of a well-trained large teacher model.
Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder
Complex dialogue mappings (CDM), including one-to-many and many-to-one mappings, tend to make dialogue models generate incoherent or dull responses, and modeling these mappings remains a huge challenge for neural dialogue systems.
Stop Filtering: Multi-View Attribute-Enhanced Dialogue Learning
However, the discarded samples may obtain high scores in other perspectives and can provide regularization effects on the model learning, which causes the performance improvement to be sensitive to the filtering ratio.
Diversifying Neural Dialogue Generation via Negative Distillation
Generative dialogue models suffer badly from the generic response problem, limiting their applications to a few toy scenarios.
Hierarchical Inductive Transfer for Continual Dialogue Learning
However, for the continual increase of online chit-chat scenarios, directly fine-tuning these models for each of the new tasks not only explodes the capacity of the dialogue system on the embedded devices but also causes knowledge forgetting on pre-trained models and knowledge interference among diverse dialogue tasks.
Generating Relevant and Coherent Dialogue Responses using Self-separated Conditional Variational AutoEncoders
Conditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the context vector with sampled latent variables.
THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses
In this work, we proposed a conversation model named "THINK" (Teamwork generation Hover around Impressive Noticeable Keywords) to make the decoder more complicated and avoid generating duplicated and self-contradicting responses.
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
The finding of general knowledge is further hindered by the unidirectional distillation, as the student should obey the teacher and may discard some knowledge that is truly general but refuted by the teacher.
Regularizing Dialogue Generation by Imitating Implicit Scenarios
Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario.
Collaborative Group Learning
Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima.
Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network
Neural conversational models learn to generate responses by taking into account the dialog history.
