Papers with DAR
Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation (2022.acl-short)
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| Challenge: | Existing sparse retrieval models rely on term-based matching to retrieve relevant documents. |
| Approach: | They propose a framework which augments the representations of documents with interpolation and perturbation. |
| Outcome: | The proposed framework significantly outperforms baselines on the dense retrieval of both the labeled and unlabeled documents. |
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. |
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition (2022.findings-acl)
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| Challenge: | Dialog sentiment classification (DSC) and dialog act recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog. |
| Approach: | They propose a framework which integrates prediction-level interactions other than semantics-level ones into dialog understanding and dual-task reasoning by integrating temporal relations into the model. |
| Outcome: | The proposed model outperforms existing models by large margins while costing less training time and requiring less computation resource. |
Integrating User History into Heterogeneous Graph for Dialogue Act Recognition (2020.coling-main)
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| Challenge: | Existing models cannot fully recognize the specific expressions given by users due to the informality and diversity of natural language expressions. |
| Approach: | They propose a Heterogeneous User History graph convolution network which utilizes the user’s historical answers grouped by DA labels as additional clues to recognize the DA label of utterances. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on two benchmark datasets and shows that it integrates user’s historical answers. |