Samyadeep Basu


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On Surgical Fine-tuning for Language Encoders
Abhilasha Lodha | Gayatri Belapurkar | Saloni Chalkapurkar | Yuanming Tao | Reshmi Ghosh | Samyadeep Basu | Dmitrii Petrov | Soundararajan Srinivasan
Findings of the Association for Computational Linguistics: EMNLP 2023

Fine-tuning all the layers of a pre-trained neural language encoder (either using all the parameters or using parameter-efficient methods) is often the de-facto way of adapting it to a new task. We show evidence that for different downstream language tasks, fine-tuning only a subset of layers is sufficient to obtain performance that is close to and often better than fine-tuning all the layers in the language encoder. We propose an efficient metric based on the diagonal of the Fisher information matrix (FIM score), to select the candidate layers for selective fine-tuning. We show, empirically on GLUE and SuperGLUE tasks and across distinct language encoders, that this metric can effectively select layers leading to a strong downstream performance. Our work highlights that task-specific information corresponding to a given downstream task is often localized within a few layers, and tuning only those is sufficient for strong performance. Additionally, we demonstrate the robustness of the FIM score to rank layers in a manner that remains constant during the optimization process.


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Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling
Samyadeep Basu | Amr Sharaf | Karine Ip Kiun Chong | Alex Fischer | Vishal Rohra | Michael Amoake | Hazem El-Hammamy | Ehi Nosakhare | Vijay Ramani | Benjamin Han
Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)

Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data to train deep learning models for such systems are not scalable. This problem can be addressed by learning from few examples using fast supervised meta-learning techniques such as prototypical networks. In this work, we systematically investigate how contrastive learning and data augmentation methods can benefit these existing meta-learning pipelines for jointly modelled IC/SF tasks. Through extensive experiments across standard IC/SF benchmarks (SNIPS and ATIS), we show that our proposed approaches outperform standard meta-learning methods: contrastive losses as a regularizer in conjunction with prototypical networks consistently outperform the existing state-of-the-art for both IC and SF tasks, while data augmentation strategies primarily improve few-shot IC by a significant margin