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A Literature Survey of Recent Advances in Chatbots
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In: Information; Volume 13; Issue 1; Pages: 41 (2022)
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Design principles and architecture of a second language learning chatbot
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Kim, Heyoung; Yang, Hyejin; Shin, Dongkwang. - : University of Hawaii National Foreign Language Resource Center, 2022. : Center for Language & Technology, 2022. : (co-sponsored by Center for Open Educational Resources and Language Learning, University of Texas at Austin), 2022
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A Therapeutic Relational Agent for Reducing Problematic Substance Use (Woebot): Development and Usability Study.
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In: Journal of medical Internet research, vol 23, iss 3 (2021)
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Multitask Transformer Model-based Fintech Customer Service Chatbot NLU System with DECO-LGG SSP-based Data ; DECO-LGG 반자동 증강 학습데이터 활용 멀티태스크 트랜스포머 모델 기반 핀테크 CS 챗봇 NLU 시스템
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In: Annual Conference on Human and Language Technology ; https://hal.archives-ouvertes.fr/hal-03603903 ; Annual Conference on Human and Language Technology, Oct 2021, Séoul, South Korea. pp.461-466 ; http://www.koreascience.or.kr/journal/OOGHAK.page (2021)
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A Methodology of Building Evaluation-Annotated Datasets (EVAD) Based on the Evaluation-Triple in E-Commerce Reviews ; 이커머스 후기글 평가분석 트리플에 기반한 평가주석 데이터셋 EVAD 구축 방법론
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In: ISSN: 1229-0343 ; The Journal of Linguistics Science ; https://hal.archives-ouvertes.fr/hal-03565731 ; The Journal of Linguistics Science, 2021, 99, pp.245-272. ⟨10.21296/jls.2021.12.99.245⟩ (2021)
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Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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Design And Evaluation of A Conversational Agent for Mental Health Support: Forming Human-Agent Sociotechnical And Therapeutic Relationships
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Förderung der Sprechfertigkeit in DaF: eine qualitative Analyse zur Nutzung von ChatClass
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In: Pandaemonium Germanicum: Revista de Estudos Germanísticos, Vol 24, Iss 42 (2021) (2021)
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The applied AI and natural language processing workshop : explore practical ways to transform your simple projects into powerful intelligent applications
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BLLDB
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UB Frankfurt Linguistik
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Hands-on natural language processing with PyTorch 1.x : build smart, AI-driven linguistic applications using deep learning and NLP techniques
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BLLDB
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UB Frankfurt Linguistik
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State Machine based Human-Bot Conversation Model and Services
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In: CAiSE 2020: 32nd International Conference on Advanced Information Systems Engineering ; https://hal.archives-ouvertes.fr/hal-03122974 ; CAiSE 2020: 32nd International Conference on Advanced Information Systems Engineering, Jun 2020, Grenoble, France. pp.199-214, ⟨10.1007/978-3-030-49435-3_13⟩ (2020)
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Spot The Bot : a robust and efficient framework for the evaluation of conversational dialogue systems ...
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A Domain-Specific Generative Chatbot Trained from Little Data
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In: Applied Sciences ; Volume 10 ; Issue 7 (2020)
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Making Chatbots More Transparent and Applicable to New Demographics
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ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ КАК АЛЬТЕРНАТИВНЫЙ РЕСУРС ДЛЯ ИЗУЧЕНИЯ ИНОСТРАННОГО ЯЗЫКА ... : Digital intelligence as an alternative resource for foreign language learning ...
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The Effect of Data Quantity on Dialog System Input Classification Models ; Datamängdens effekt på modeller för avsiktsklassificering i chattkonversationer
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Abstract:
This paper researches how different amounts of data affect different word vector models for classification of dialog system user input. A hypothesis is tested that there is a data threshold for dense vector models to reach the state-of-the-art performance that have been shown with recent research, and that character-level n-gram word-vector classifiers are especially suited for Swedish classifiers–because of compounding and the character-level n-gram model ability to vectorize out-of-vocabulary words. Also, a second hypothesis is put forward that models trained with single statements are more suitable for chat user input classification than models trained with full conversations. The results are not able to support neither of our hypotheses but show that sparse vector models perform very well on the binary classification tasks used. Further, the results show that 799,544 words of data is insufficient for training dense vector models but that training the models with full conversations is sufficient for single statement classification as the single-statement- trained models do not show any improvement in classifying single statements. ; Detta arbete undersöker hur olika datamängder påverkar olika slags ordvektormodeller för klassificering av indata till dialogsystem. Hypotesen att det finns ett tröskelvärde för träningsdatamängden där täta ordvektormodeller när den högsta moderna utvecklingsnivån samt att n-gram-ordvektor-klassificerare med bokstavs-noggrannhet lämpar sig särskilt väl för svenska klassificerare söks bevisas med stöd i att sammansättningar är särskilt produktiva i svenskan och att bokstavs-noggrannhet i modellerna gör att tidigare osedda ord kan klassificeras. Dessutom utvärderas hypotesen att klassificerare som tränas med enkla påståenden är bättre lämpade att klassificera indata i chattkonversationer än klassificerare som tränats med hela chattkonversationer. Resultaten stödjer ingendera hypotes utan visar istället att glesa vektormodeller presterar väldigt väl i de genomförda klassificeringstesterna. Utöver detta visar resultaten att datamängden 799 544 ord inte räcker till för att träna täta ordvektormodeller väl men att konversationer räcker gott och väl för att träna modeller för klassificering av frågor och påståenden i chattkonversationer, detta eftersom de modeller som tränats med användarindata, påstående för påstående, snarare än hela chattkonversationer, inte resulterar i bättre klassificerare för chattpåståenden.
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Keyword:
Chatbot; Chattbot; Chatterbot; Dialog System; Dialogsystem; Language Technology (Computational Linguistics); Natural Language Understanding; Naturlig språkbehandling; Ordinbäddning; Ordvektormodeller; Språkteknologi (språkvetenskaplig databehandling); Text Classification; Textklassificering; Virtual Assistant; Virtuell Assistent; Word Embedding; Word Vector Models
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URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237282
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Modelação e prototipagem de ChatBots ; Modeling and prototyping ChatBots
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