402 citations
- Zhejiang UniversityCN32 papers
- Alibaba Group (China)CN18 papers
- Peking UniversityCN13 papers
- Bellevue Hospital CenterUS12 papers
- Chinese Academy of SciencesCN10 papers
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67 papers · 1 filter
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval
Linlong Xu, Baosong Yang, Xiaoyu Lv +3
Interactive and non-interactive model are the two de-facto standard frameworks in vector-based cross-lingual information retrieval (V-CLIR), which embed queries and documents in sy…
GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation
Xuming Lin, Shaobo Cui, Zhongzhou Zhao +3
Existing data-driven methods can well handle short text generation. However, when applied to the long-text generation scenarios such as story generation or advertising text generat…
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification
ChengCheng Han, Zeqiu Fan, Dongxiang Zhang +3
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the…
Transferable Dialogue Systems and User Simulators
Bo-Hsiang Tseng, Yinpei Dai, Florian Kreyssig +1
One of the difficulties in training dialogue systems is the lack of training data. We explore the possibility of creating dialogue data through the interaction between a dialogue s…
Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings
Xinyu Wang, Zixia Jia, Yong Jiang +1
This paper describes the system used in submission from SHANGHAITECH team to the IWPT 2021 Shared Task. Our system is a graph-based parser with the technique of Automated Concatena…
EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model
Chenye Cui, Yi Ren, Jinglin Liu +4
Recently, there has been an increasing interest in neural speech synthesis. While the deep neural network achieves the state-of-the-art result in text-to-speech (TTS) tasks, how to…