• Refine your search:
  • 
    Keyword:
    
    • Computation and Language cs.CL (9)
    • FOS Computer and information sciences (9)
    • Computational Linguistics (2)
    • Machine Learning (2)
    • Natural Language Processing (2)
    • Artificial Intelligence cs.AI (1)
    • Condensed Matter Physics (1)
    • Data Management System (1)
    • Deep Learning (1)
    • Electromagnetism (1)
    •   more
  • 
    Creator / Publisher
  • 
    Year
  • 
    Medium
  • 
    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 – 12 of 12 1 AUTOLEX: An Automatic Framework for Linguistic Exploration ... Chaudhary, Aditi; Sheikh, Zaid; Mortensen, David R. - : arXiv, 2022 BASE Show details 2 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... Pratapa, Adithya; Anastasopoulos, Antonios; Rijhwani, Shruti. - : arXiv, 2021 BASE Show details 3 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Chaudhary, Aditi. - : Underline Science Inc., 2021 BASE Show details 4 Do Context-Aware Translation Models Pay the Right Attention? ... Yin, Kayo; Fernandes, Patrick; Pruthi, Danish. - : arXiv, 2021 BASE Show details 5 When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection ... Chaudhary, Aditi; Yin, Kayo; Anastasopoulos, Antonios. - : arXiv, 2021 BASE Show details 6 When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Chaudhary, Aditi. - : Underline Science Inc., 2021 BASE Show details 7 Do Context-Aware Translation Models Pay the Right Attention? ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chaudhary, Aditi; Fernandes, Patrick. - : Underline Science Inc., 2021 BASE Show details 8 DICT-MLM: Improved Multilingual Pre-Training using Bilingual Dictionaries ... Chaudhary, Aditi; Raman, Karthik; Srinivasan, Krishna. - : arXiv, 2020 BASE Show details 9 SIGTYP 2020 Shared Task: Prediction of Typological Features ... Bjerva, Johannes; Salesky, Elizabeth; Mielke, Sabrina J.. - : arXiv, 2020 BASE Show details 10 Automatic Extraction of Rules Governing Morphological Agreement ... Chaudhary, Aditi; Anastasopoulos, Antonios; Pratapa, Adithya. - : arXiv, 2020 BASE Show details 11 A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization ... Neubig, Graham; Rijhwani, Shruti; Palmer, Alexis. - : arXiv, 2020 BASE Show details 12 Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations ... Chaudhary, Aditi; Zhou, Chunting; Levin, Lori; Neubig, Graham; Mortensen, David R.; Carbonell, Jaime G.. - : arXiv, 2018 Abstract: Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging. We present two approaches for improving generalization to low-resourced languages by adapting continuous word representations using linguistically motivated subword units: phonemes, morphemes and graphemes. Our method requires neither parallel corpora nor bilingual dictionaries and provides a significant gain in performance over previous methods relying on these resources. We demonstrate the effectiveness of our approaches on Named Entity Recognition for four languages, namely Uyghur, Turkish, Bengali and Hindi, of which Uyghur and Bengali are low resource languages, and also perform experiments on Machine Translation. Exploiting subwords with transfer learning gives us a boost of +15.2 NER F1 for Uyghur and +9.7 F1 for Bengali. We also show improvements in the monolingual setting where we achieve (avg.) +3 F1 and (avg.) +1.35 BLEU. ... : Accepted at EMNLP 2018 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1808.09500 https://dx.doi.org/10.48550/arxiv.1808.09500 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 12 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0