Doan Nam Long Vu — accepted AI-conference papers

2024

Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation

Doan Nam Long Vu, Timour Igamberdiev, Ivan Habernal

EMNLP 2024finding

Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual data points during training is becoming increasingly popular in NLP. However, the choice of granularity at which DP is applied is often neglected. For example, neural machine translation (NMT) typically operate…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2022

Layer or Representation Space: What Makes BERT-based Evaluation Metrics Robust?

Doan Nam Long Vu, Nafise Sadat Moosavi, Steffen Eger

COLING 2022main

The evaluation of recent embedding-based evaluation metrics for text generation is primarily based on measuring their correlation with human evaluations on standard benchmarks. However, these benchmarks are mostly from similar domains to those used for pretraining word embeddings. This raises concer…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗