219 citations · 448 across the 14 of their papers we have counts for
7 papers · 1 filter
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation
Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao +5
Building models of natural language processing (NLP) is challenging in low-resource scenarios where only limited data are available. Optimization-based meta-learning algorithms ach…
Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation
Zhiliang Tian, Wei Bi, Dongkyu Lee +4
Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing…
When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications
Zequn Liu, Ruiyi Zhang, Yiping Song +2
Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low…
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
Yiping Song, Zequn Liu, Wei Bi +2
Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems. Existing methods tend to use the meta-learning frame…
Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems
Yiping Song, Rui Yan, Xiang Li +2
Open-domain human-computer conversation has attracted much attention in the field of NLP. Contrary to rule- or template-based domain-specific dialog systems, open-domain conversati…
Dialogue Session Segmentation by Embedding-Enhanced TextTiling
Yiping Song, Lili Mou, Rui Yan +4
In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation. Howe…
Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
Lili Mou, Yiping Song, Rui Yan +3
Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactor…