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Domain-specific language understanding requires integrating multiple piecesof relevant contextual information. For example, we see both suicide anddepression-related behavior (multiple contexts) in the text ``I have a gun andfeel pretty bad about my life, and it wouldn't be the worst thing if I didn'twake up tomorrow''. Domain specificity in self-attention architectures ishandled by fine-tuning on excerpts from relevant domain specific resources(datasets and external knowledge - medical textbook chapters on mental healthdiagnosis related to suicide and depression). We propose a modifiedself-attention architecture Knowledge-infused Self Attention Transformer (KSAT)that achieves the integration of multiple domain-specific contexts through theuse of external knowledge sources. KSAT introduces knowledge-guided biases indedicated self-attention layers for each knowledge source to accomplish this.In addition, KSAT provides mechanics for controlling the trade-off betweenlearning from data and learning from knowledge. Our quantitative andqualitative evaluations show that (1) the KSAT architecture provides novelhuman-understandable ways to precisely measure and visualize the contributionsof the infused domain contexts, and (2) KSAT performs competitively with otherknowledge-infused baselines and significantly outperforms baselines that usefine-tuning for domain-specific tasks.

KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts