activity
20192023
most citedPersonalized Query Rewriting in Conversational AI Agents

5 citations · 14 across the 11 of their papers we have counts for

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Showing cs.CLShow all

6 papers · 1 filter

cs.CL2023

PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer

Xu Han, Bin Guo, Yoon Jung +4

Personalized dialogue agents (DAs) powered by large pre-trained language models (PLMs) often rely on explicit persona descriptions to maintain personality consistency. However, suc…

cs.CL20221 cited

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

Pragaash Ponnusamy, Clint Solomon Mathialagan, Gustavo Aguilar +2

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

cs.CL2022

A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning

Md Mofijul Islam, Gustavo Aguilar, Pragaash Ponnusamy +3

Subword tokenization is a commonly used input pre-processing step in most recent NLP models. However, it limits the models' ability to leverage end-to-end task learning. Its freque…

cs.CL2021

Learning to Selectively Learn for Weakly-supervised Paraphrase Generation

Kaize Ding, Dingcheng Li, Alexander Hanbo Li +4

Paraphrase generation is a longstanding NLP task that has diverse applications for downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on larg…

cs.CL20201 cited

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

Yunmo Chen, Sixing Lu, Fan Yang +3

Query rewriting (QR) systems are widely used to reduce the friction caused by errors in a spoken language understanding pipeline. However, the underlying supervised models require…

cs.CL20204 cited

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

Zheng Chen, Xing Fan, Yuan Ling +2

Query rewriting (QR) is an increasingly important technique to reduce customer friction caused by errors in a spoken language understanding pipeline, where the errors originate fro…