Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions (2020.coling-main)
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| Challenge: | a novel context-aware dynamic convolution network is proposed to better leverage the local contexts when dynamically generating convolution kernels. |
| Approach: | They propose a dynamic convolution network to leverage local contexts when generating convolution kernels. |
| Outcome: | The proposed frameworks achieve state-of-the-art on two benchmark datasets. |
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| Challenge: | Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. |
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DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition (2022.findings-acl)
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| Challenge: | Considerable work on Dialogue Act Classification (DAC) has been done on textual inputs. |
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| Challenge: | Emotion recognition in conversation (ERC) has received much attention lately due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. |
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Xuanqing Liu, Luyang Kong, Wei Niu, Afshin Khashei, Belinda Zeng, Steve Johnson, Jon Jay, Davor Golac, Matt Pope
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| Challenge: | Sentence-level emotion detection is a challenging task due to subjectivity of emotion. |
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A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks (L18-1)
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| Challenge: | Existing models of dialogue act classification work on the utterance-level and only very few consider context. |
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| Challenge: | a new corpus of conversations is being developed to support data visualization exploration . we use data augmentation to improve our methods for dialogue act classification . |
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| Challenge: | Social media are a gold mine for researchers in many domains and especially in natural language processing . license restrictions make it difficult to strictly reproduce research results on Twitter data . |
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