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slot tagging: CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling ... . Slots Configurations — ACE Agent - docs.nvidia.com. TLT/Jarvis - Intent Detection & Slot Tagging | NVIDIA NGC. [1812.10628] Intent Detection and Slots Prompt in a Closed-Domain Chatbot.

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CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling ...

He, K., Yan, Y., Xu, W.: From context-aware to knowledge-aware: boosting OOV tokens recognition in slot tagging with background knowledge. Neurocomputing 445, 267–275 (2021).

Slots Configurations — ACE Agent - docs.nvidia.com

ACE Agent facilitates the detection and handling of slots using a file called slots.yaml. You can define the slots you want to support, as well as attributes such as memory and rules for slot tagging and/or slot manipulation.

TLT/Jarvis - Intent Detection & Slot Tagging | NVIDIA NGC

Slot Filling: It equals to slot tagging if all values of slots can be aligned into input sentence. Otherwise, the value of slot should be predicted in a classification or generation way.

[1812.10628] Intent Detection and Slots Prompt in a Closed-Domain Chatbot

In this paper, we introduce a methodology for predicting intent and slots of a query for a chatbot that answers career-related queries. We take a multi-staged approach where both the processes (intent-classification and slot-tagging) inform each other's decision-making in different stages. The model breaks down the problem into stages, solving one problem at a time and passing on relevant results of the current stage to the next, thereby reducing search space for subsequent stages, and eventually making classification and tagging more viable after each stage. We also observe that relaxing rules for a fuzzy entity-matching in slot-tagging after each stage (by maintaining a separate Named Entity Tagger per stage) helps us improve performance, although at a slight cost of false-positives. Our model has achieved state-of-the-art performance with F1-score of 77.63% for intent-classification and 82.24% for slot-tagging on our dataset that we would publicly release along with the paper.

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Sequential Labeling for Tracking Dynamic Dialog States | PDF

1) The document discusses sequential labeling for tracking dynamic dialog states in conversations. It focuses on three dialog state tracking challenges: goals tracking, method tracking, and requested slots tracking. 2) The method presented uses conditional random fields (CRFs) with a BIO tagging scheme and sequential labeling to predict dialog states. Features include SLU hypotheses and system actions. 3) Experimental results on the DSTC 2 dataset show the CRF models outperform maximum entropy baselines on goals and method tracking in accuracy and error, and achieve comparable results to the baseline on requested slots tracking. - Download as a PDF or view online for free

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