674 citations
- University of Illinois ChicagoUS13 papers
- Singapore Management UniversitySG11 papers
- Carnegie Mellon UniversityUS8 papers
- Georgia Institute of TechnologyUS7 papers
- Princeton UniversityUS7 papers
- Nanyang Technological UniversitySG6 papers
- University of Illinois Urbana-ChampaignUS6 papers
- Agency for Science, Technology and ResearchSG5 papers
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- Beihang UniversityCN4 papers
27 papers · 1 filter
Cascaded Fast and Slow Models for Efficient Semantic Code Search
Akhilesh Deepak Gotmare, Junnan Li, Shafiq Joty +1
The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query. Existing approa…
Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks
Le Xue, Mingfei Gao, Zeyuan Chen +2
We propose a novel framework to evaluate the robustness of transformer-based form field extraction methods via form attacks. We introduce 14 novel form transformations to evaluate…
Deconfounded Causal Collaborative Filtering
Shuyuan Xu, Juntao Tan, Shelby Heinecke +2
Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation pe…
Multimodal Conditionality for Natural Language Generation
Michael Sollami, Aashish Jain
Large scale pretrained language models have demonstrated state-of-the-art performance in language understanding tasks. Their application has recently expanded into multimodality le…
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding
Shiyang Li, Semih Yavuz, Wenhu Chen +1
Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amo…
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approache…