Author name cluster

Saloni Potdar

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2022 Conference Paper

Improved Text Classification via Contrastive Adversarial Training

Lin Pan
Chung-Wei Hang
Avirup Sil
Saloni Potdar

We propose a simple and general method to regularize the fine-tuning of Transformer-based encoders for text classification tasks. Specifically, during fine-tuning we generate adversarial examples by perturbing the word embedding matrix of the model and perform contrastive learning on clean and adversarial examples in order to teach the model to learn noiseinvariant representations. By training on both clean and adversarial examples along with the additional contrastive objective, we observe consistent improvement over standard fine-tuning on clean examples. On several GLUE benchmark tasks, our fine-tuned BERTLarge model outperforms BERTLarge baseline by 1. 7% on average, and our fine-tuned RoBERTaLarge improves over RoBERTaLarge baseline by 1. 3%. We additionally validate our method in different domains using three intent classification datasets, where our fine-tuned RoBERTaLarge outperforms RoBERTaLarge baseline by 1–2% on average. For the challenging low-resource scenario, we train our system using half of the training data (per intent) in each of the three intent classification datasets, and achieve similar performance compared to the baseline trained with full training data.

PDF Details

AAAI Conference 2017 Conference Paper

Neural Models for Sequence Chunking

Feifei Zhai
Saloni Potdar
Bing Xiang
Bowen Zhou

Many natural language understanding (NLU) tasks, such as shallow parsing (i. e. , text chunking) and semantic slot ﬁlling, require the assignment of representative labels to the meaningful chunks in a sentence. Most of the current deep neural network (DNN) based methods consider these tasks as a sequence labeling problem, in which a word, rather than a chunk, is treated as the basic unit for labeling. These chunks are then inferred by the standard IOB (Inside-Outside- Beginning) labels. In this paper, we propose an alternative approach by investigating the use of DNN for sequence chunking, and propose three neural models so that each chunk can be treated as a complete unit for labeling. Experimental results show that the proposed neural sequence chunking models can achieve start-of-the-art performance on both the text chunking and slot ﬁlling tasks.

PDF Details