Search Results for author: Guanglu Wan

Found 12 papers, 4 papers with code

DESED: Dialogue-based Explanation for Sentence-level Event Detection

1 code implementation COLING 2022 Yinyi Wei, Shuaipeng Liu, Jianwei Lv, Xiangyu Xi, Hailei Yan, Wei Ye, Tong Mo, Fan Yang, Guanglu Wan

Many recent sentence-level event detection efforts focus on enriching sentence semantics, e. g., via multi-task or prompt-based learning.

Dialogue Generation Event Detection

MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts

1 code implementation25 Nov 2022 Xiangyu Xi, Jianwei Lv, Shuaipeng Liu, Wei Ye, Fan Yang, Guanglu Wan

As a pioneering exploration that expands event detection to the scenarios involving informal and heterogeneous texts, we propose a new large-scale Chinese event detection dataset based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.

Event Detection

Peak-First CTC: Reducing the Peak Latency of CTC Models by Applying Peak-First Regularization

no code implementations7 Nov 2022 Zhengkun Tian, Hongyu Xiang, Min Li, Feifei Lin, Ke Ding, Guanglu Wan

To reduce the peak latency, we propose a simple and novel method named peak-first regularization, which utilizes a frame-wise knowledge distillation function to force the probability distribution of the CTC model to shift left along the time axis instead of directly modifying the calculation process of CTC loss and gradients.

Knowledge Distillation

A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example

no code implementations13 May 2022 Xiangyu Xi, Chenxu Lv, Yuncheng Hua, Wei Ye, Chaobo Sun, Shuaipeng Liu, Fan Yang, Guanglu Wan

Though widely used in industry, traditional task-oriented dialogue systems suffer from three bottlenecks: (i) difficult ontology construction (e. g., intents and slots); (ii) poor controllability and interpretability; (iii) annotation-hungry.

Chatbot Task-Oriented Dialogue Systems

CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming ASR

1 code implementation31 Mar 2022 Keyu An, Huahuan Zheng, Zhijian Ou, Hongyu Xiang, Ke Ding, Guanglu Wan

The simulation module is jointly trained with the ASR model using a self-supervised loss; the ASR model is optimized with the usual ASR loss, e. g., CTC-CRF as used in our experiments.

Chunking speech-recognition +1

An Empirical Study of Language Model Integration for Transducer based Speech Recognition

no code implementations31 Mar 2022 Huahuan Zheng, Keyu An, Zhijian Ou, Chen Huang, Ke Ding, Guanglu Wan

Based on the DR method, we propose a low-order density ratio method (LODR) by replacing the estimation with a low-order weak language model.

Language Modelling speech-recognition +1

Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss

no code implementations17 Mar 2022 Yantao Gong, Cao Liu, Fan Yang, Xunliang Cai, Guanglu Wan, Jiansong Chen, Weipeng Zhang, Houfeng Wang

Experiments on the open datasets verify that our model outperforms the existing calibration methods and achieves a significant improvement on the calibration metric.

Intent Detection

Multi-speaker Multi-style Text-to-speech Synthesis With Single-speaker Single-style Training Data Scenarios

no code implementations23 Dec 2021 Qicong Xie, Tao Li, Xinsheng Wang, Zhichao Wang, Lei Xie, Guoqiao Yu, Guanglu Wan

Moreover, the explicit prosody features used in the prosody predicting module can increase the diversity of synthetic speech by adjusting the value of prosody features.

Speech Synthesis Style Transfer +1

Learning Speaker Embedding with Momentum Contrast

1 code implementation7 Jan 2020 Ke Ding, Xuanji He, Guanglu Wan

Momentum Contrast (MoCo) is a recently proposed unsupervised representation learning framework, and has shown its effectiveness for learning good feature representation for downstream vision tasks.

Representation Learning Speaker Verification

Cannot find the paper you are looking for? You can Submit a new open access paper.