• Refine your search:
  • 
    Keyword:
    
    • Computational Linguistics (1)
    • Conversational Agent (1)
    • Language Models (1)
    • Machine Learning (1)
    • Machine Learning and Data Mining (1)
    • Natural Language Processing (1)
    • Natural Language Understanding (1)
  • 
    Creator / Publisher
  • 
    Year:
    
    • 2021 (1)
  • 
    Medium
  • 
    Type
  • 
    BLLDB-Access:
    • free (1)
    • subject to license (0)

Search in the Catalogues and Directories

All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 1 of 1 1 A Label-Aware BERT Attention Network for Zero-Shot Multi-Intent Detection in Spoken Language Understanding ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Juang, Biing-Hwang; Su, Ruolin; Wu, Ting-Wei. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.399/ Abstract: With the early success of query-answer assistants such as Alexa and Siri, research attempts to expand system capabilities of handling service automation are now abundant. However, preliminary systems have quickly found the inadequacy in relying on simple classification techniques to effectively accomplish the automation task. The main challenge is that the dialogue often involves complexity in user’s intents (or purposes) which are multiproned, subject to spontaneous change, and difficult to track. Furthermore, public datasets have not considered these complications and the general semantic annotations are lacking which may result in zero-shot problem. Motivated by the above, we propose a Label-Aware BERT Attention Network (LABAN) for zero-shot multi-intent detection. We first encode input utterances with BERT and construct a label embedded space by considering embedded semantics in intent labels. An input utterance is then ... Keyword: Computational Linguistics; Conversational Agent; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Natural Language Understanding URL: https://dx.doi.org/10.48448/c470-pm50 https://underline.io/lecture/37352-a-label-aware-bert-attention-network-for-zero-shot-multi-intent-detection-in-spoken-language-understanding BASE Hide details

Mobile view All Catalogues UB Frankfurt Linguistik 0 IDS Mannheim 0 OLC Linguistik 0 UB Frankfurt Retrokatalog 0 DNB Subject Category Language 0 Institut für Empirische Sprachwissenschaft 0 Leibniz-Centre General Linguistics (ZAS) 0 Bibliographies BLLDB 0 BDSL 0 IDS Bibliografie zur deutschen Grammatik 0 IDS Bibliografie zur Gesprächsforschung 0 IDS Konnektoren im Deutschen 0 IDS Präpositionen im Deutschen 0 IDS OBELEX meta 0 MPI-SHH Linguistics Collection 0 MPI for Psycholinguistics 0 Linked Open Data catalogues Annohub 0 Online resources Link directory 0 Journal directory 0 Database directory 0 Dictionary directory 0 Open access documents BASE 1 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0