Cross-domain stance detection via shared label embedding
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Solution Overview
Problem
Cross-domain stance detection is hindered by variations in label inventories, annotation setups, and data collection methods across different domains, making it difficult to adapt standard domain adaptation approaches effectively, especially for predicting unseen user-defined labels.
Innovation Solution
An end-to-end framework for cross-domain label-adaptive stance detection that combines domain adaptation techniques like Mixture of Experts (MoE) and domain-adversarial training with label embeddings, enabling unsupervised learning and out-of-domain prediction using a shared encoder and label embedding layer to produce probability distributions for stance classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard domain adaptation approaches are used, then in-domain performance may be maintained, but out-of-domain prediction for unseen labels fails due to label inventory variations
Solution Approach 1:
The model segments the encoding process into domain-specific encoder blocks and a shared label embedding layer. Each domain expert encoder block processes input independently, then their outputs are combined. This segmentation allows domain adaptation while maintaining a unified label representation space that generalizes to unseen domains and labels.
Solution Approach 2:
The label embedding layer serves as a universal component that handles all labels across all domains. By sharing this layer across domains and making it adaptive to different label inventories, the system achieves multi-functionality that enables both in-domain and out-of-domain prediction for seen and unseen labels alike.
2Measurement precision
If domain-specific models are trained for each domain, then domain-specific accuracy improves, but parameter requirements and memory needs increase significantly
Solution Approach 1:
The model merges multiple domain-specific encoder blocks with a shared label embedding layer into a unified architecture. The domain expert encoder blocks process domain-specific features while the shared label embedding layer handles label representations. This combining approach maintains domain-specific accuracy while significantly reducing the total number of parameters compared to fully separate domain models.
Solution Approach 2:
The shared label embedding layer serves multiple domains simultaneously, making it a universal component that reduces parameter redundancy. This multi-functional design allows the same parameters to be used across different domains while domain-specific encoder blocks handle domain variations, achieving efficiency without sacrificing accuracy.
3Adaptability or versatility
If separate encoders are used for each domain, then domain adaptation is simplified, but device complexity and training difficulty increase
Solution Approach 1:
The architecture segments encoding functions into domain expert encoder blocks that can be independently trained and adapted. Each block can be trained on its specific domain data, simplifying the adaptation process. The segmented design allows flexible configuration where domain-specific components can be swapped or adjusted without affecting the entire system.
Solution Approach 2:
The shared label embedding layer acts as an intermediary that connects domain-specific encoder blocks. This mediator component harmonizes the outputs from different domain encoders and maps them to a unified label space, simplifying the integration process and reducing the complexity of coordinating multiple separate models.
4Measurement precision
If full supervision is required for label adaptation, then label prediction accuracy improves, but the system cannot handle unseen user-defined labels
Solution Approach 1:
The model performs preliminary learning of label representations through the shared label embedding layer during training on seen labels. This preliminary action creates a foundation of label knowledge that can be adapted to unseen labels without requiring full supervision. The pre-learned embedding space enables the model to generalize to new labels by leveraging the structural relationships learned during training.
Solution Approach 2:
The label embedding layer uses parameter adaptation techniques to adjust its representations for different domains and label inventories. By changing the parameters of the shared embedding layer based on domain characteristics rather than requiring complete retraining with full supervision, the system achieves accurate prediction for both seen and unseen labels in a more flexible manner.
Data Source
AI summary
Cross-domain label-adaptive stance detection is provided by receiving a natural language input; tokenizing the natural language input by a shared tokenizer to identify tokens in the natural language input; parsing the tokens by a plurality of domain expert encoder blocks to produce a corresponding plurality of domain encodings for the natural language input; parsing the tokens by a global encoder block to produce a global encoding for the natural language input; processing the plurality of domain encodings and the global encoding by a label embedding layer to produce a probability distribution for a stance of the natural language input; and outputting the stance for the natural language input.