206 citations · 347 across the 17 of their papers we have counts for
17 papers
MERGED: Multimodal Entity Resolution via Generated Expert Reasoning Distillation
You-Lin Chen, Kyoungjun Park, Bin Xu +3
In product entity resolution, relationship definitions constantly evolve with business needs, yet adapting to each change traditionally requires slow, costly human annotation that…
Learning variant product relationship and variation attributes from e-commerce website structures
Pedro Herrero-Vidal, You-Lin Chen, Cris Liu +2
We introduce VARM, variant relationship matcher strategy, to identify pairs of variant products in e-commerce catalogs. Traditional definitions of entity resolution are concerned w…
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning
Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura +8
Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do n…
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations
Bingsheng Yao, Prithviraj Sen, Lucian Popa +2
Human-annotated labels and explanations are critical for training explainable NLP models. However, unlike human-annotated labels whose quality is easier to calibrate (e.g., with a…
A Closer Look at the Calibration of Differentially Private Learners
Hanlin Zhang, Xuechen Li, Prithviraj Sen +2
We systematically study the calibration of classifiers trained with differentially private stochastic gradient descent (DP-SGD) and observe miscalibration across a wide range of vi…
Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks
Prithviraj Sen, Breno W. S. R. de Carvalho, Ryan Riegel +1
Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data. While some proposals approxi…