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1
Personalized Transformer for Explainable Recommendation ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.383 Abstract: Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item IDs are important identifiers for personalization. Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words. To address this problem, we present a PErsonalized Transformer for Explainable Recommendation (PETER), on which we design a simple and effective learning objective that utilizes the IDs to predict the words in the target explanation, so as to endow the IDs with linguistic meanings and to achieve personalized Transformer. Besides generating explanations, PETER can also make recommendations, which makes it a unified model for the whole recommendation-explanation ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/p847-qk07
https://underline.io/lecture/25705-personalized-transformer-for-explainable-recommendation
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2
Learning Shared Semantic Space for Speech-to-Text Translation ...
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3
Glancing Transformer for Non-Autoregressive Neural Machine Translation ...
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4
Probabilistic Graph Reasoning for Natural Proof Generation ...
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5
Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker ...
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6
Language Tags Matter for Zero-Shot Neural Machine Translation ...
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7
Learning Language Specific Sub-network for Multilingual Machine Translation ...
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8
Contrastive Aligned Joint Learning for Multilingual Summarization ...
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9
Contrastive Learning for Many-to-many Multilingual Neural Machine Translation ...
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