Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks
Distributed representation plays an important role in deep learning based natural language processing. However, the representation of a sentence often varies in different tasks, which is usually learned from scratch and suffers from the limited amounts of training data. In this paper, we claim that a good sentence representation should be invariant and can benefit the various subsequent tasks. To achieve this purpose, we propose a new scheme of information sharing for multi-task learning. More specifically, all tasks share the same sentence representation and each task can select the task-specific information from the shared sentence representation with attention mechanism. The query vector of each task's attention could be either static parameters or generated dynamically. We conduct extensive experiments on 16 different text classification tasks, which demonstrate the benefits of our architecture.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationMulti-Task LearningRepresentation LearningSentencetext-classificationText ClassificationSimilar Papers 제목 키워드 기반
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-noising by designing neural networks with …
RelationRelation ExtractionSentenceSentence EmbeddingsMulti-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications
Multi-player multi-armed bandits (MMAB) study how decentralized players cooperatively play the same multi-armed bandit so as to maximize their total cumulative rewards. Existing MMAB models mostly assume when more than o…
Edge-computingMulti-Armed BanditsExemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification
In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL ai…
General Classificationimage-classificationImage ClassificationCascading Multiway Attentions for Document-level Sentiment Classification
Document-level sentiment classification aims to assign the user reviews a sentiment polarity. Previous methods either just utilized the document content without consideration of user and product information, or did not c…
ClassificationGeneral ClassificationProduct RecommendationSentence+2Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms
We generalize the multiple-play multi-armed bandits (MP-MAB) problem with a shareable arm setting, in which several plays can share the same arm. Furthermore, each shareable arm has a finite reward capacity and a ''per-l…
Multi-Armed Bandits