Papers with context-aware attention module
Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling (2020.acl-main)
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| Challenge: | Existing methods for Sequence Labeling require high-quality annotations, but imperfect annotations are relatively easy to obtain from crowdsourcing (noisy labels) Existing approaches to learn a model without knowing the underlying ground truth label sequences in the target domain are expensive and time-consuming. |
| Approach: | They propose a framework Consensus Network that can be trained on annotations from multiple sources. |
| Outcome: | The proposed framework improves on learning with crowd annotations and unsupervised cross-domain model adaptation in two practical settings. |
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis (D19-1)
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| Challenge: | Multi-modal analysis is a field emerging in the fields of natural language processing, computer vision and speech processing . multimodal analysis uses a variety of information from multiple sources to build efficient systems . acoustic and visual information can provide better information for classification decisions . |
| Approach: | They propose a recurrent neural network based approach for multi-modal sentiment and emotion analysis . they employ a context-aware attention module to exploit the correspondence among neighboring utterances . |
| Outcome: | The proposed model learns inter-modal interaction among participating modalities through auto-encoder mechanism . it is compared with existing state-of-the-art models on five standard multi-modal affect analysis datasets . |