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Related Experiment Video

Updated: Sep 30, 2025

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

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Published on: September 5, 2019

A Deep Learning-Based Chinese Semantic Parser for the Almond Virtual Assistant.

Shih-Wei Liao¹, Cheng-Han Hsu¹, Jeng-Wei Lin²

¹Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.

Sensors (Basel, Switzerland)

|March 10, 2022

Summary

This summary is machine-generated.

Researchers extended Genie to support Chinese for the Almond virtual assistant, enabling natural language interaction with Internet services and IoT devices. The MQAN model significantly improved Chinese command translation accuracy.

Keywords:

Chinese Genie ThingTalk deep learning multiple question answer network semantic parsing virtual assistant

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Area of Science:

Natural Language Processing
Human-Computer Interaction
Artificial Intelligence

Background:

Almond is an open-source virtual assistant for accessing Internet services and Internet of Things (IoT) devices.
ThingTalk is a programming language for virtual assistants, and Thingpedia is an application encyclopedia.
Training data for Almond's neural network was synthesized using the Genie system.

Purpose of the Study:

To extend the Genie system for synthesizing Chinese natural language commands and corresponding ThingTalk programs.
To train and evaluate machine learning models for translating Chinese commands into ThingTalk.
To enable Chinese language support for the Almond virtual assistant.

Main Methods:

Developed 649 Chinese primitive and 292 construct templates for 107 devices and 261 functions in Thingpedia.
Trained two models: sequence-to-sequence (seq2seq) and multiple question answer network (MQAN).
Evaluated model performance using exact match, BLEU, and F1 token accuracy metrics.

Main Results:

The MQAN model demonstrated superior performance compared to the seq2seq model.
MQAN achieved an exact match accuracy of 0.7, BLEU score of 0.82, and F1 token accuracy of 0.88.
Successfully enabled Chinese language interaction with services and devices in Almond.

Conclusions:

The extended Genie system effectively supports Chinese language for virtual assistant command synthesis.
MQAN is a more effective model for translating Chinese natural language commands into ThingTalk programs.
Chinese users can now leverage Almond for seamless access to connected services and IoT devices.