41 citations · 63 across the 8 of their papers we have counts for
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Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
Kunbo Ding, Weijie Liu, Yuejian Fang +3
Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.…
DORB: Dynamically Optimizing Multiple Rewards with Bandits
Ramakanth Pasunuru, Han Guo, Mohit Bansal
Policy gradients-based reinforcement learning has proven to be a promising approach for directly optimizing non-differentiable evaluation metrics for language generation tasks. How…
Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits
Han Guo, Ramakanth Pasunuru, Mohit Bansal
Domain adaptation performance of a learning algorithm on a target domain is a function of its source domain error and a divergence measure between the data distribution of these tw…
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning
Han Guo, Ramakanth Pasunuru, Mohit Bansal
Multi-task learning (MTL) has achieved success over a wide range of problems, where the goal is to improve the performance of a primary task using a set of relevant auxiliary tasks…
Dynamic Multi-Level Multi-Task Learning for Sentence Simplification
Han Guo, Ramakanth Pasunuru, Mohit Bansal
Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified se…
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation
Han Guo, Ramakanth Pasunuru, Mohit Bansal
An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document. We improve these important aspects…