Papers with learning architecture

2 papers
Multitask Parsing Across Semantic Representations (P18-1)

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Challenge: UCCA parsing is a test case for multitask learning, with auxiliary tasks AMR, SDP and Universal Dependencies (UD) . Semantic parsers have arguably yet to reach their full potential due to the limited amount of semantically annotated training data.
Approach: They propose a general transition-based parser that can parse UCCA, AMR, SDP and Universal Dependencies (UD) they use a transition-driven learning architecture and a uniform transition-basic learning architecture to train the parsers.
Outcome: The proposed parser improves UCCA, AMR, SDP and Universal Dependencies (UD) parsing over training in English, German and French.
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis (D19-1)

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Challenge: Emotion cause analysis aims to identify the reasons behind emotions . previous models focus on learning architecture with local textual information .
Approach: They propose a method to extract emotion cause with hierarchical neural model and knowledge-based regularizations by sentiment lexicon and common knowledge.
Outcome: The proposed method outperforms baselines on two public datasets in different languages and outperformed competitive baselines by 2.08%.

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