Search Results for author: Megh Thakkar

Found 8 papers, 6 papers with code

HypMix: Hyperbolic Interpolative Data Augmentation

1 code implementation EMNLP 2021 Ramit Sawhney, Megh Thakkar, Shivam Agarwal, Di Jin, Diyi Yang, Lucie Flek

Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities.

Adversarial Robustness Data Augmentation

CIAug: Equipping Interpolative Augmentation with Curriculum Learning

1 code implementation NAACL 2022 Ramit Sawhney, Ritesh Soun, Shrey Pandit, Megh Thakkar, Sarvagya Malaviya, Yuval Pinter

CIAug achieves state-of-the-art results over existing interpolative augmentation methods on 10 benchmark datasets across 4 languages in text classification and named-entity recognition tasks.

Data Augmentation named-entity-recognition +4

DMix: Adaptive Distance-aware Interpolative Mixup

1 code implementation ACL 2022 Ramit Sawhney, Megh Thakkar, Shrey Pandit, Ritesh Soun, Di Jin, Diyi Yang, Lucie Flek

Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities. We extend Mixup and propose DMix, an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space.

Data Augmentation Sentence Classification

Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations

1 code implementation16 Nov 2022 Linlin Liu, Xingxuan Li, Megh Thakkar, Xin Li, Shafiq Joty, Luo Si, Lidong Bing

Due to the huge amount of parameters, fine-tuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios.

Chart-to-Text: A Large-Scale Benchmark for Chart Summarization

2 code implementations ACL 2022 Shankar Kantharaj, Rixie Tiffany Ko Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty

We also introduce a number of state-of-the-art neural models as baselines that utilize image captioning and data-to-text generation techniques to tackle two problem variations: one assumes the underlying data table of the chart is available while the other needs to extract data from chart images.

Data-to-Text Generation Image Captioning

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