Search Results for author: Chenlei Guo

Found 13 papers, 1 papers with code

Contextual Rephrase Detection for Reducing Friction in Dialogue Systems

no code implementations EMNLP 2021 Zhuoyi Wang, Saurabh Gupta, Jie Hao, Xing Fan, Dingcheng Li, Alexander Hanbo Li, Chenlei Guo

Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e. g. users’ implicit feedback).

Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI

no code implementations29 Apr 2022 Pragaash Ponnusamy, Clint Solomon Mathialagan, Gustavo Aguilar, Chengyuan Ma, Chenlei Guo

Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean.

Data Augmentation graph construction +1

Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems

no code implementations22 Feb 2022 Zhongkai Sun, Sixing Lu, Chengyuan Ma, Xiaohu Liu, Chenlei Guo

However, these methods rarely focus on query expansion and entity weighting simultaneously, which may limit the scope and accuracy of the query reformulation retrieval.

voice assistant

Incremental user embedding modeling for personalized text classification

no code implementations13 Feb 2022 Ruixue Lian, Che-Wei Huang, Yuqing Tang, Qilong Gu, Chengyuan Ma, Chenlei Guo

Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education.

Classification Multi-class Classification +2

VAE based Text Style Transfer with Pivot Words Enhancement Learning

1 code implementation6 Dec 2021 Haoran Xu, Sixing Lu, Zhongkai Sun, Chengyuan Ma, Chenlei Guo

Text Style Transfer (TST) aims to alter the underlying style of the source text to another specific style while keeping the same content.

Style Transfer Text Style Transfer

Learning to Selectively Learn for Weakly-supervised Paraphrase Generation

no code implementations EMNLP 2021 Kaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan, Chenlei Guo, Yang Liu, Huan Liu

In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data.

Language Modelling Meta-Learning +1

Pattern-aware Data Augmentation for Query Rewriting in Voice Assistant Systems

no code implementations21 Dec 2020 Yunmo Chen, Sixing Lu, Fan Yang, Xiaojiang Huang, Xing Fan, Chenlei Guo

Query rewriting (QR) systems are widely used to reduce the friction caused by errors in a spoken language understanding pipeline.

Data Augmentation Spoken Language Understanding +1

Personalized Query Rewriting in Conversational AI Agents

no code implementations9 Nov 2020 Alireza Roshan-Ghias, Clint Solomon Mathialagan, Pragaash Ponnusamy, Lambert Mathias, Chenlei Guo

Spoken language understanding (SLU) systems in conversational AI agents often experience errors in the form of misrecognitions by automatic speech recognition (ASR) or semantic gaps in natural language understanding (NLU).

Automatic Speech Recognition Natural Language Understanding +1

Pre-Training for Query Rewriting in A Spoken Language Understanding System

no code implementations13 Feb 2020 Zheng Chen, Xing Fan, Yuan Ling, Lambert Mathias, Chenlei Guo

Then, inspired by the wide success of pre-trained contextual language embeddings, and also as a way to compensate for insufficient QR training data, we propose a language-modeling (LM) based approach to pre-train query embeddings on historical user conversation data with a voice assistant.

Entity Resolution Speech Recognition +2

Feedback-Based Self-Learning in Large-Scale Conversational AI Agents

no code implementations6 Nov 2019 Pragaash Ponnusamy, Alireza Roshan Ghias, Chenlei Guo, Ruhi Sarikaya

Typically, the accuracy of the ML models in these components are improved by manually transcribing and annotating data.

Collaborative Filtering Self-Learning

Knowledge Distillation from Internal Representations

no code implementations8 Oct 2019 Gustavo Aguilar, Yuan Ling, Yu Zhang, Benjamin Yao, Xing Fan, Chenlei Guo

In this paper, we propose to distill the internal representations of a large model such as BERT into a simplified version of it.

Knowledge Distillation

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