Search Results for author:
Found 10 papers, 3 papers with code
Data Collection vs. Knowledge Graph Completion: What is Needed to Improve Coverage?
Given such estimates, WordNet’s coverage is remarkable.
Training on Lexical Resources
We propose using lexical resources (thesaurus, VAD) to fine-tune pretrained deep nets such as BERT and ERNIE.
Interpreting Graph Neural Networks with In-Distributed Proxies
A popular paradigm for the explainability of GNNs is to identify explainable subgraphs by comparing their labels with the ones of original graphs.
Random Walk on Multiple Networks
To take advantage of rich information in multiple networks and make better inferences on entities, in this study, we propose random walk on multiple networks, RWM.
Probing the limit of hydrologic predictability with the Transformer network
While the Transformer results are not higher than current state-of-the-art, we still learned some valuable lessons: (1) the vanilla Transformer architecture is not suitable for hydrologic modeling; (2) the proposed recurrence-free modification can improve Transformer performance so future work can continue to test more of such modifications; and (3) the prediction limits on the dataset should be close to the current state-of-the-art model.
Data-Driven Adaptive Simultaneous Machine Translation
In simultaneous translation (SimulMT), the most widely used strategy is the wait-k policy thanks to its simplicity and effectiveness in balancing translation quality and latency.
W-CTC: a Connectionist Temporal Classification Loss with Wild Cards
Standard CTC computes a loss by aggregating over all possible alignment paths, that map the entire sequence to the entire label (full alignment).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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On Attention Redundancy: A Comprehensive Study
(What) We define and focus the study on redundancy matrices generated from pre-trained and fine-tuned BERT-base model for GLUE datasets.
Isotropy in the Contextual Embedding Space: Clusters and Manifolds
We hope the study in this paper could provide insights towards a better understanding of the deep language models.
Attentive Social Recommendation: Towards User And Item Diversities
Social recommendation system is to predict unobserved user-item rating values by taking advantage of user-user social relation and user-item ratings.
