Papers by Guchun Zhang

3 papers
Multi 3 WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems (2023.tacl-1)

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Challenge: Task-oriented dialog (TOD) is one of the central objectives, hallmarks, and applications of machine intelligence.
Approach: They propose a multilingual, multi-domain, multiparallele ToD dataset that offers culturally adapted dialogs in 4 languages for training and evaluation of multilingual and cross-lingual systems.
Outcome: The proposed dataset is large-scale and culturally adapted to enable training and evaluation of multilingual and cross-lingual ToD systems.
A Systematic Study of Performance Disparities in Multilingual Task-Oriented Dialogue Systems (2023.emnlp-main)

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Challenge: Existing systems trained for Arabic or Turkish using annotated data fully parallel to English ToD data still exhibit diminished ToD task performance.
Approach: They define new quantitative measures of absolute and relative equivalence in system performance, capturing disparities across languages and within individual languages.
Outcome: The proposed measures capture disparities across languages and within individual languages.
Text-to-Code Generation with Modality-relative Pre-training (2024.eacl-long)

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Challenge: Large pre-trained language models have been applied to programming language tasks with great success, often through further pre-training of a strictly-natural language model.
Approach: They propose to map programming language modalities into the same embedding space by separating embeddable spaces between modality and modality-relative training objectives.
Outcome: The proposed model can be adapted and represented differently depending on which modality they belong to and to the ultimate benefit of the downstream task.

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