25 citations · 105 across the 19 of their papers we have counts for
17 papers · 1 filter
WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain
Raj Sanjay Shah, Kunal Chawla, Dheeraj Eidnani +7
Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training s…
Geographic Citation Gaps in NLP Research
Mukund Rungta, Janvijay Singh, Saif M. Mohammad +1
In a fair world, people have equitable opportunities to education, to conduct scientific research, to publish, and to get credit for their work, regardless of where they live. Howe…
Robustness of Demonstration-based Learning Under Limited Data Scenario
Hongxin Zhang, Yanzhe Zhang, Ruiyi Zhang +1
Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario. Simply augmenting the input with some demonst…
DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification
Hui Chen, Wei Han, Diyi Yang +1
This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification. DoubleMix fi…
SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models
Jingfeng Yang, Haoming Jiang, Qingyu Yin +3
Recent research showed promising results on combining pretrained language models (LMs) with canonical utterance for few-shot semantic parsing. The canonical utterance is often leng…
SUBS: Subtree Substitution for Compositional Semantic Parsing
Jingfeng Yang, Le Zhang, Diyi Yang
Although sequence-to-sequence models often achieve good performance in semantic parsing for i.i.d. data, their performance is still inferior in compositional generalization. Severa…