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Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao
This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN can be prohibitively expensive. Here we apply the knowledge distillation method (Hinton et al., 2015) in the multi-task learning setting. For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to \emph{distill} knowledge from these ensemble teachers. We show that the distilled MT-DNN significantly outperforms the original MT-DNN on 7 out of 9 GLUE tasks, pushing the GLUE benchmark (single model) to 83.7\% (1.5\% absolute improvement\footnote{ Based on the GLUE leaderboard at https://gluebenchmark.com/leaderboard as of April 1, 2019.}). The code and pre-trained models will be made publicly available at https://github.com/namisan/mt-dnn.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | MultiNLI | MT-DNN-ensemble | Matched | 87.9 | #19 of 67 | Archive leaderboard | report |
| Natural Language Inference | MultiNLI | MT-DNN-ensemble | Mismatched | 87.4 | #19 of 67 | Archive leaderboard | report |
| Semantic Textual Similarity | SentEval | MT-DNN-ensemble | MRPC | 92.7/90.3 | #5 of 6 | Archive leaderboard | report |
| Semantic Textual Similarity | SentEval | MT-DNN-ensemble | SICK-E | - | #5 of 6 | Archive leaderboard | report |
| Semantic Textual Similarity | SentEval | MT-DNN-ensemble | SICK-R | - | #5 of 6 | Archive leaderboard | report |
| Semantic Textual Similarity | SentEval | MT-DNN-ensemble | STS | 91.1/90.7* | #5 of 6 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | MT-DNN-ensemble | Accuracy | 96.5 | #15 of 87 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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