SCALE disentangles emotion and cause representations in conversations and uses optimal transport for many-to-many global alignment, achieving SOTA on ECPEC benchmarks.
In: Proceedings of the 2019 Conference on Empirical Methods in Natural Lan- guageProcessingandthe9thInternationalJointConferenceonNaturalLanguage Processing (EMNLP-IJCNLP)
2 Pith papers cite this work. Polarity classification is still indexing.
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MSEA uses a master-slave encoder architecture on patent specifications and claims, enhanced with pointer networks and repetition suppression, to generate better summaries as measured by small ROUGE score gains.
citing papers explorer
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Emotion-Cause Pair Extraction in Conversations via Semantic Decoupling and Graph Alignment
SCALE disentangles emotion and cause representations in conversations and uses optimal transport for many-to-many global alignment, achieving SOTA on ECPEC benchmarks.
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The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims
MSEA uses a master-slave encoder architecture on patent specifications and claims, enhanced with pointer networks and repetition suppression, to generate better summaries as measured by small ROUGE score gains.