Peng Lu


pdf bib
Improving Generalization of Pre-trained Language Models via Stochastic Weight Averaging
Peng Lu | Ivan Kobyzev | Mehdi Rezagholizadeh | Ahmad Rashid | Ali Ghodsi | Phillippe Langlais
Findings of the Association for Computational Linguistics: EMNLP 2022

Knowledge Distillation (KD) is a commonly used technique for improving the generalization of compact Pre-trained Language Models (PLMs) on downstream tasks. However, such methods impose the additional burden of training a separate teacher model for every new dataset.Alternatively, one may directly work on the improvement of the optimization procedure of the compact model towards better generalization. Recent works observe that the flatness of the local minimum correlates well with better generalization.In this work, we adapt Stochastic Weight Averaging (SWA), a method encouraging convergence to a flatter minimum, to fine-tuning PLMs. We conduct extensive experiments on various NLP tasks (text classification, question answering, and generation) and different model architectures and demonstrate that our adaptation improves the generalization without extra computation cost. Moreover, we observe that this simple optimization technique is able to outperform the state-of-the-art KD methods for compact models.


pdf bib
RW-KD: Sample-wise Loss Terms Re-Weighting for Knowledge Distillation
Peng Lu | Abbas Ghaddar | Ahmad Rashid | Mehdi Rezagholizadeh | Ali Ghodsi | Philippe Langlais
Findings of the Association for Computational Linguistics: EMNLP 2021

Knowledge Distillation (KD) is extensively used in Natural Language Processing to compress the pre-training and task-specific fine-tuning phases of large neural language models. A student model is trained to minimize a convex combination of the prediction loss over the labels and another over the teacher output. However, most existing works either fix the interpolating weight between the two losses apriori or vary the weight using heuristics. In this work, we propose a novel sample-wise loss weighting method, RW-KD. A meta-learner, simultaneously trained with the student, adaptively re-weights the two losses for each sample. We demonstrate, on 7 datasets of the GLUE benchmark, that RW-KD outperforms other loss re-weighting methods for KD.


pdf bib
SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling
Peng Lu | Ting Bai | Philippe Langlais
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Multi-task learning (MTL) has been studied recently for sequence labeling. Typically, auxiliary tasks are selected specifically in order to improve the performance of a target task. Jointly learning multiple tasks in a way that benefit all of them simultaneously can increase the utility of MTL. In order to do so, we propose a new LSTM cell which contains both shared parameters that can learn from all tasks, and task-specific parameters that can learn task-specific information. We name it a Shared-Cell Long-Short Term Memory SC-LSTM. Experimental results on three sequence labeling benchmarks (named-entity recognition, text chunking, and part-of-speech tagging) demonstrate the effectiveness of our SC-LSTM cell.