Papers by Minh-Quang Pham
Gradient-based Gradual Pruning for Language-Specific Multilingual Neural Machine Translation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Multilingual neural machine translation suffers from performance degradation in high-resource languages compared to bilingual counterparts. |
| Approach: | They propose a gradient-based gradual pruning technique for multilingual neural machine translation that allows for partial parameter sharing across language pairs to alleviate interference. |
| Outcome: | The proposed approach yields a notable performance gain on IWSLT and WMT datasets. |
Latent Group Dropout for Multilingual and Multidomain Machine Translation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Multidomain and multilingual machine translation often rely on parameter sharing strategies, which are hardcoded in the network architecture, independent of the similarities between tasks. |
| Approach: | They propose a method to take advantage of similarities by using a latent-variable model and develop techniques to train this model end-to-end. |
| Outcome: | The proposed model improves translation performance without increasing the model size. |
Select, Prompt, Filter: Distilling Large Language Models for Summarizing Conversations (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) can be expensive to train, deploy, and use for specific natural language generation tasks. |
| Approach: | They propose a method to distill ChatGPT and fine-tune smaller LMs for summarizing forum conversations using a semantic similarity metric. |
| Outcome: | The proposed method leads to significant improvements of up to 6.6 ROUGE-2 score by leveraging sufficient in-domain pseudo-labeled data over standard KD approach given the same size of training data. |