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Detecting the user's intent and finding the corresponding slots among theutterance's words are important tasks in natural language understanding. Theirinterconnected nature makes their joint modeling a standard part of trainingsuch models. Moreover, data scarceness and specialized vocabularies poseadditional challenges. Recently, the advances in pre-trained language models,namely contextualized models such as ELMo and BERT have revolutionized thefield by tapping the potential of training very large models with just a fewsteps of fine-tuning on a task-specific dataset. Here, we leverage such models,namely BERT and RoBERTa, and we design a novel architecture on top of them.Moreover, we propose an intent pooling attention mechanism, and we reinforcethe slot filling task by fusing intent distributions, word features, and tokenrepresentations. The experimental results on standard datasets show that ourmodel outperforms both the current non-BERT state of the art as well as somestronger BERT-based baselines.

Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection