5 citations · 19 across the 14 of their papers we have counts for
14 papers
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…
CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer
Ruolin Su, Zhongkai Sun, Sixing Lu +2
Recent advances in cross-lingual commonsense reasoning (CSR) are facilitated by the development of multilingual pre-trained models (mPTMs). While mPTMs show the potential to encode…
KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting
Jinglun Cai, Mingda Li, Ziyan Jiang +5
Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions. When there is an entity error, it imposes extra challenges for a dialogue system…
PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding
Niranjan Uma Naresh, Ziyan Jiang, Ankit +4
Conversational understanding is an integral part of modern intelligent devices. In a large fraction of the global traffic from customers using smart digital assistants, frictions i…
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…
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…
Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems
Zhongkai Sun, Sixing Lu, Chengyuan Ma +2
Voice assistants such as Alexa, Siri, and Google Assistant have become increasingly popular worldwide. However, linguistic variations, variability of speech patterns, ambient acous…
Incremental user embedding modeling for personalized text classification
Ruixue Lian, Che-Wei Huang, Yuqing Tang +3
Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, an…
VAE based Text Style Transfer with Pivot Words Enhancement Learning
Haoran Xu, Sixing Lu, Zhongkai Sun +2
Text Style Transfer (TST) aims to alter the underlying style of the source text to another specific style while keeping the same content. Due to the scarcity of high-quality parall…
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…
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…
Personalized Query Rewriting in Conversational AI Agents
Alireza Roshan-Ghias, Clint Solomon Mathialagan, Pragaash Ponnusamy +2
Spoken language understanding (SLU) systems in conversational AI agents often experience errors in the form of misrecognitions by automatic speech recognition (ASR) or semantic gap…
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…
Feedback-Based Self-Learning in Large-Scale Conversational AI Agents
Pragaash Ponnusamy, Alireza Roshan Ghias, Chenlei Guo +1
Today, most large-scale conversational AI agents (e.g. Alexa, Siri, or Google Assistant) are built using manually annotated data to train the different components of the system. Ty…