Quynh Do
10 papers · 2019–2023 · 6 conferences · across top CS/AI conferences
Achievements
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πΊοΈ Taxonomy Completionist (18) π Interdisciplinary Bridge π Cross-Pollinator (13) π Conference Polyglot (6) π§ Keyword Pioneer
πΊοΈ
Taxonomy Completionist
(18)
π€
Dynamic Duo
(10)
π
Trend Setter
π₯
Unstoppable
(5)
π
Century Club
(10)
β
The Questioner
(2)
Conferences
INTERSPEECH (3)
ACL (2)
EMNLP (2)
COLING (1)
EACL (1)
IJCNLP (1)
Top co-authors
Research topics
Keywords
spoken language understanding
(10)
slot filling
(2)
cross-lingual transfer learning
(2)
intent classification
(2)
language model
(2)
domain generalization
(1)
transfer learning
(1)
domain adaptation
(1)
knowledge transfer
(1)
knowledge distillation
(1)
speech recognition
(1)
cross-lingual transfer
(1)
synthetic data generation
(1)
distribution drift
(1)
distributionally robust optimization
(1)
data balancing
(1)
data selection
(1)
model drift
(1)
model deployment
(1)
production system
(1)
Papers
Sharing Encoder Representations across Languages, Domains and Tasks in Large-Scale Spoken Language Understanding
ACL 2023
Distributionally Robust Finetuning BERT for Covariate Drift in Spoken Language Understanding
ACL 2022
Towards Need-Based Spoken Language Understanding Model Updates: What Have We Learned?
EMNLP 2022
Predicting Temporal Performance Drop of Deployed Production Spoken Language Understanding Models
INTERSPEECH 2021
The Impact of Intent Distribution Mismatch on Semi-Supervised Spoken Language Understanding
INTERSPEECH 2021
The impact of domain-specific representations on BERT-based multi-domain spoken language understanding
EACL 2021
To What Degree Can Language Borders Be Blurred In BERT-based Multilingual Spoken Language Understanding?
COLING 2020
Data Balancing for Boosting Performance of Low-Frequency Classes in Spoken Language Understanding
INTERSPEECH 2020
Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding
IJCNLP 2019
Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding
EMNLP 2019