Ming Sun


Neural Text Normalization with Subword Units
Courtney Mansfield | Ming Sun | Yuzong Liu | Ankur Gandhe | Björn Hoffmeister
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)

Text normalization (TN) is an important step in conversational systems. It converts written text to its spoken form to facilitate speech recognition, natural language understanding and text-to-speech synthesis. Finite state transducers (FSTs) are commonly used to build grammars that handle text normalization. However, translating linguistic knowledge into grammars requires extensive effort. In this paper, we frame TN as a machine translation task and tackle it with sequence-to-sequence (seq2seq) models. Previous research focuses on normalizing a word (or phrase) with the help of limited word-level context, while our approach directly normalizes full sentences. We find subword models with additional linguistic features yield the best performance (with a word error rate of 0.17%).


AppDialogue: Multi-App Dialogues for Intelligent Assistants
Ming Sun | Yun-Nung Chen | Zhenhao Hua | Yulian Tamres-Rudnicky | Arnab Dash | Alexander Rudnicky
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Users will interact with an individual app on smart devices (e.g., phone, TV, car) to fulfill a specific goal (e.g. find a photographer), but users may also pursue more complex tasks that will span multiple domains and apps (e.g. plan a wedding ceremony). Planning and executing such multi-app tasks are typically managed by users, considering the required global context awareness. To investigate how users arrange domains/apps to fulfill complex tasks in their daily life, we conducted a user study on 14 participants to collect such data from their Android smart phones. This document 1) summarizes the techniques used in the data collection and 2) provides a brief statistical description of the data. This data guilds the future direction for researchers in the fields of conversational agent and personal assistant, etc. This data is available at http://AppDialogue.com.


Conversational Strategies for Robustly Managing Dialog in Public Spaces
Aasish Pappu | Ming Sun | Seshadri Sridharan | Alexander Rudnicky
Proceedings of the EACL 2014 Workshop on Dialogue in Motion