Xin Lu

Xin Lu
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Main area of research
Natural language & AI

Xin Lu has worked in the Natural Language Processing field for almost 14 years since the last year of her undergraduate. She started her NLP footsteps from machine translation, text-to-speech systems and voice biometrics in Harbin Institute of Technology in China. After Xin graduated there with a Master's degree in Computer Science, she joined the NLP lab in University of Illinois at Chicago in USA for her Ph.D study in intelligent tutoring systems. This gave Xin an opportunity to enter the research field of dialog systems and cognitive science . Before joining Nuance, Xin worked in a Montreal based company building the back-end dialog system for a conversational 3D game. Her journey with Nuance started with DragonGo and ADK. Currently she is working in Chinese NLU team for supporting the products on mobile devices.

Selected articles

Automatic Detection of Prosody Phrase Boundaries for Text-to-Speech Systems

Automatic acquisition of the prosodic phrase boundary detecting rules from the text and speech corpora has always been a difficulty for TTS systems. We collected

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Dealing with Polyphone in Text-to-Speech Systems Using How-Net

Dealing with polyphones is an important part of Chinese text-to-speech system. Because the pronunciation of a Chinese character is directly related to the meaning of

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Explanatory Content and Multi-Turn Dialogues in Tutoring

We aim to systematically investigate the effects of explanatory content and multi-turn dialogues on learning during tutoring by implementing alternative tutoring regimens in an intelligent

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Positive and negative verbal feedback for Intelligent Tutoring Systems

We built three different versions of an ITS on a letter pattern extrapolation task: in one version, students only receive color-coded feedback; in the second,

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Toward a Computational Model of Expert Tutoring: A First Report

We are exploring the differences between expert and less expert tutors with two goals: cognitive (what does tutoring tell us about learning) and applied (which

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