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Sequential Labeling for Tracking Dynamic Dialog States | PDF
Gujarati Task Oriented Dialogue Slot Tagging Using Deep Neural Network Models | SpringerLink
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[1812.10628] Intent Detection and Slots Prompt in a Closed-Domain ...
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Advanced Joint Model for Vietnamese Intent Detection and Slot Tagging | SpringerLink
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[1812.10628] Intent Detection and Slots Prompt in a Closed-Domain Chatbot
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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

Gujarati Task Oriented Dialogue Slot Tagging Using Deep Neural Network Models | SpringerLink

In this paper, the primary focus is of Slot Tagging of Gujarat Dialogue, which enables the Gujarati language communication between human and machine, allowing machines to perform given task and provide desired output. The accuracy of tagging entirely depends on...

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BKAI-NAVER Challenge 2022 - Vietnamese Intent Detection and Slot ...

Neural-based context-aware models for slot tagging tasks in language understanding have achieved state-of-the-art performance, especially deep contextualized models, such as ELMo, ...

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BKAI-NAVER Challenge 2022 là hoạt động thuộc dự án hợp tác nghiên cứu AI giữa Trung tâm BKAI, Trường CNTT&TT và Tập đoàn Naver (Hàn Quốc). Dữ liệu cung cấp trong cuộc thi là một phần kết quả trong các dự án hợp tác năm 2021 giữa hai bên. Cuộc thi gồm ba tác vụ: Trích xuất ý định và slot trong câu lệnh người dùng (Vietnamese Intent Detection and Slot Tagging); Khoanh vùng cơ thể và nhận dạng cử chỉ động (Body Segmentation and Gesture Recognition); Phát hiện và nhận diện văn bản Tiếng Việt trong ảnh khung cảnh (Vietnamese Scene Text Detection and Recognition).

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Advanced Joint Model for Vietnamese Intent Detection and Slot Tagging | SpringerLink

This paper aims to propose BiJoint-BERT-NLU, an advanced BERT-based joint model for Vietnamese intent detection and slot tagging, which extends the state-of-the-art JointBERT-CRF model. This model leverages the bi-directional relationships of these two tasks by: (i)...

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This paper aims to propose BiJoint-BERT-NLU, an advanced BERT-based joint model for Vietnamese intent detection and slot tagging, which extends the state-of-the-art JointBERT-CRF model.

[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.