Unsupervised dialogue intent detection via hierarchical topic model

Popov, Artem; Bulatov, Victor; Polyudova, Darya; Veselova, Eugenia

doi:10.26615/978-954-452-056-4_108

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…The usefulness of these agents was at peak during pandemic when the life was restricted without human assistance for any problem. For designing these agents two main components are required namely slot filling (Firdaus et al, 2021;Manchanda et al, 2021) and intent detection (Popov et al, 2019;Reza et al, 2020) and their performance have a direct effect on human language understanding and efficiently performing various natural language related tasks (Yang et al, 2021;Abro et al,2022). Most of the research considers existing datasets and apply their slot filling and intent detection models on them either separately or in an integrated way.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: The Context-based approach of Slot Label Creation and Slot Filling for Interactive Systems

Magoo

Singh

2023

Preprint

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Slot-filling is considered one of the most important components of task-based interactive systems. The main challenge in slot filling is to assign a semantic label to each text in the utterance which is further useful for generating the response by the interactive system. Another challenge is the non-availability of extensive labeled data. Thus in this research, we primarily focus on developing two approaches namely (i) automatic slot identification through IOB labeling for slot labels creation and preparing a labeled dataset (ii) a self-learning based model where the slot labels are created in step (i) are predicted for an untrained query from the same or different domains. We use BI-LSTM with Elmo Embedding for our proposed frameworks with POS Tags to generate slot-independent tags and label descriptions for slot labeling. We applied and compared our model on the SNIPS and Mobile service provider dataset through the F1 score and analyzed that the efficiency of self-learning-based models highly depends upon the quality and correlation among different domains of the same dataset.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: The Context-based approach of Slot Label Creation and Slot Filling for Interactive Systems

Magoo

Singh

2023

Preprint

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“…These approaches model the user intent and then propose novel items to the user by assigning weights to the intent-related information resources. 71,[205][206][207][208][209][210] They exploit user profiles, cross-platform learning models, textual intent mapping, and contextual weight models to learn user intent and generate personalized recommendations.…”

Section: Intent-aware Modelingmentioning

confidence: 99%

“…Popov et al 210 consider a language model to characterize the user preferred query terms and attribute values to evaluate preferred entity features for the intent. Therefore, intent consists of captured vocabularies of both the query and entity spaces.…”

Section: Taxonomy Of Iars Applications and Techniquesmentioning

confidence: 99%

Developing smart city services using intent‐aware recommendation systems: A survey

Rafique

Hafid

Qadir

2023

Trans Emerging Tel Tech

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Smart cities could be defined as urban areas that use Information and Communication Technology (ICT) to solve city problems in efficient and sustainable ways. Intent-aware Recommender Systems (IARS) within ICT play a crucial role in filtering useless information according to user demands and assist in decision-making in various smart city platforms. In smart cities, the user traces on IoT, RFIDs, mobiles, and smart sensors capture actual user intent of performing an activity and enhance user satisfaction by proposing optimal services. This paper presents a detailed literature survey of the field of IARS and how it can be used for developing smart city services. First, we present the evolution of IARS with the development of computing technology. Then, we present case studies, synergies, advances, and a reference implementation architecture of IARS for smart cities. We discuss requirements for developing smart city services using IARS. Furthermore, we devise a comprehensive taxonomy of applications and techniques of IARS using different performance parameters. Finally, we elaborate on current issues, challenges, and future research directions in IARS; these directions we believe will pave the way for autonomous service provisioning in smart cities.

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“…Baseline methods: We employ two state-of-the-art baseline methods for performance comparison against our proposed artefact detection approaches: (i) K-Means clustering (Popov et al 2019) -an unsupervised approach in which conversation utterances are clustered over TF-IDF vector space and utterance labels are assigned based on majority voting for each cluster. We use it as a baseline against our unsupervised artefact extraction approach (DKG); and (ii) BiLSTM-CRF model (Kumar et al 2018) -a supervised approach where utterance representations are learned using the BiLSTM model which are then used for predicting utterance labels modeled as a sequence labelling task.…”

Section: Artefact Predictionmentioning

confidence: 99%

Picking Pearl from Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services

Azad¹,

Ghosh²,

Gupta³

et al. 2022

AAAI

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Site Reliability Engineers (SREs) play a key role in identifying the cause of an issue and preforming remediation steps to resolve it. After an issue is reported, SREs come together in a virtual room (collaboration platform) to triage the issue. While doing so, they leave behind a wealth of information, in the form of conversations, which can be used later for triaging similar issues. However, usability of these conversations offer challenges due to them being and scarcity of conversation utterance label. This paper presents a novel approach for issue artefact extraction from noisy conversations with minimal labelled data. We propose a combination of unsupervised and supervised models with minimal human intervention that leverages domain knowledge to predict artefacts for a small amount of conversation data and use that for fine-tuning an already pre-trained language model for artefact prediction on a large amount of conversation data. Experimental results on our dataset show that the proposed ensemble of the unsupervised and supervised models is better than using either one of them individually. We also present a deployment case study of the proposed artefact prediction.

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Unsupervised dialogue intent detection via hierarchical topic model

Cited by 9 publications

References 17 publications

RETRACTED: The Context-based approach of Slot Label Creation and Slot Filling for Interactive Systems

RETRACTED: The Context-based approach of Slot Label Creation and Slot Filling for Interactive Systems

Developing smart city services using intent‐aware recommendation systems: A survey

Picking Pearl from Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services

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