• Refine your search:
  • 
    Keyword
  • 
    Creator / Publisher:
    
    • Ding , Kaize (1)
    • Fan, Xing (1)
    • Guo, Chenlei (1)
    • Li, Alexander Hanbo (1)
    • Li, Dingcheng (1)
    • Liu, Huan (1)
    • Liu, Yang (1)
    • The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (1)
  • 
    Year
  • 
    Medium:
    
    • Online (1)
  • 
    Type
  • 
    BLLDB-Access

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 Learning to Selectively Learn for Weakly-supervised Paraphrase Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ding , Kaize; Fan, Xing; Guo, Chenlei; Li, Alexander Hanbo; Li, Dingcheng; Liu, Huan; Liu, Yang. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.480/ Abstract: Paraphrase generation is a longstanding NLP task that has diverse applications on downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though unsupervised endeavors have been proposed to alleviate this issue, they may fail to generate meaningful paraphrases due to the lack of supervision signals. In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with data of weak supervision. Specifically, we tackle the weakly-supervised paraphrase generation problem by: (1) obtaining abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion; and (2) developing a meta-learning framework to progressively select valuable samples for fine-tuning a pre-trained language model BART on the sentential paraphrasing task. We demonstrate that our approach achieves significant ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://dx.doi.org/10.48448/d4jr-k311 https://underline.io/lecture/37365-learning-to-selectively-learn-for-weakly-supervised-paraphrase-generation 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