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How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation ...
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Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining ...
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Prefix-Tuning: Optimizing Continuous Prompts for Generation ...
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HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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Ara-Women-Hate: The first Arabic Hate Speech corpus regarding Women ...
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Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL ...
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130 |
Explanations for CommonsenseQA: New Dataset and Models ...
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Societal Biases in Language Generation: Progress and Challenges ...
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HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
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Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.223/ Abstract: News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users' historical news click behavior data, which may lead to privacy concerns and hazards. Federated Learning is a privacy-preserving framework for multiple clients to collaboratively train models without sharing their private data. However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients. In this paper, we propose an efficient federated learning framework for privacy-preserving news recommendation. Instead of training and communicating the whole model, we decompose the news recommendation model into a large news model maintained in the server and a light-weight user model shared on both server and clients, where news representations and user model are communicated between server and clients. ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/y05k-v407 https://underline.io/lecture/37627-efficient-fedrec-efficient-federated-learning-framework-for-privacy-preserving-news-recommendation
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UserAdapter: Few-Shot User Learning in Sentiment Analysis ...
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Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
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GEM: Natural Language Generation, Evaluation, and Metrics - Part 2 ...
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Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings ...
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