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Kees van Deemter

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Guanyi Chen

> Home > Persons > Kees van Deemter

Publications

2024
[i23]
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  - journals/corr/abs-2402-07432
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Guanyi Chen, Fahime Same, Kees van Deemter:
Intrinsic Task-based Evaluation for Referring Expression Generation. CoRR abs/2402.07432 (2024)
[i22]
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  - journals/corr/abs-2403-04376
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Yuqi Liu, Guanyi Chen, Kees van Deemter:
Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases. CoRR abs/2403.04376 (2024)
2023
[j26]
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  - journals/csl/ChenSD23
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Guanyi Chen, Fahime Same, Kees van Deemter:
Neural referential form selection: Generalisability and interpretability. Comput. Speech Lang. 79: 101466 (2023)
[j24]
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  - journals/jair/ChenD23
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Guanyi Chen, Kees van Deemter:
Computational Modelling of Quantifier Use: Corpus, Models, and Evaluation. J. Artif. Intell. Res. 77: 167-206 (2023)
[c81]
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  - conf/inlg/SameCD23
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Fahime Same, Guanyi Chen, Kees van Deemter:
Models of reference production: How do they withstand the test of time? INLG 2023: 93-105
[i17]
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  - journals/corr/abs-2307-14817
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Fahime Same, Guanyi Chen, Kees van Deemter:
Models of reference production: How do they withstand the test of time? CoRR abs/2307.14817 (2023)
2022
[c78]
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  - conf/acl/SameCD22
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Fahime Same, Guanyi Chen, Kees van Deemter:
Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems. ACL (1) 2022: 5554-5567
[c77]
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  - conf/eval4nlp/ChenSD22
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Guanyi Chen, Fahime Same, Kees van Deemter:
Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset. Eval4NLP 2022: 103-114
[c76]
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  - conf/ijcnlp/ChenD22
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Guanyi Chen, Kees van Deemter:
Understanding the Use of Quantifiers in Mandarin. AACL/IJCNLP (Findings) 2022: 73-80
[i16]
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  - journals/corr/abs-2203-08274
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Fahime Same, Guanyi Chen, Kees van Deemter:
Non-neural Models Matter: A Re-evaluation of Neural Referring Expression Generation Systems. CoRR abs/2203.08274 (2022)
[i12]
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  - journals/corr/abs-2209-11977
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Guanyi Chen, Kees van Deemter:
Understanding the Use of Quantifiers in Mandarin. CoRR abs/2209.11977 (2022)
[i11]
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  - journals/corr/abs-2210-04828
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Guanyi Chen, Fahime Same, Kees van Deemter:
Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset. CoRR abs/2210.04828 (2022)
2021
[c74]
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  - conf/inlg/ChenSD21
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Guanyi Chen, Fahime Same, Kees van Deemter:
What can Neural Referential Form Selectors Learn? INLG 2021: 154-166
[c73]
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  - conf/inlg/JarnforsCDS21
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Jani Järnfors, Guanyi Chen, Kees van Deemter, Rint Sybesma:
Using BERT for choosing classifiers in Mandarin. INLG 2021: 172-176
[i9]
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  - journals/corr/abs-2108-06806
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Guanyi Chen, Fahime Same, Kees van Deemter:
What can Neural Referential Form Selectors Learn? CoRR abs/2108.06806 (2021)
2020
[c69]
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  - conf/inlg/ChenD20
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Guanyi Chen, Kees van Deemter:
Lessons from Computational Modelling of Reference Production in Mandarin and English. INLG 2020: 263-272
[c68]
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  - conf/inlg/MiltenburgLKGCL20
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Emiel van Miltenburg, Wei-Ting Lu, Emiel Krahmer, Albert Gatt, Guanyi Chen, Lin Li, Kees van Deemter:
Gradations of Error Severity in Automatic Image Descriptions. INLG 2020: 398-411
[i6]
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  - journals/corr/abs-2011-07398
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Guanyi Chen, Kees van Deemter:
Lessons from Computational Modelling of Reference Production in Mandarin and English. CoRR abs/2011.07398 (2020)
2019
[c64]
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  - conf/inlg/ChenDPSL19
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Guanyi Chen, Kees van Deemter, Silvia Pagliaro, Louk Smalbil, Chenghua Lin:
QTUNA: A Corpus for Understanding How Speakers Use Quantification. INLG 2019: 124-129
[c63]
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Guanyi Chen, Kees van Deemter, Chenghua Lin:
Generating Quantified Descriptions of Abstract Visual Scenes. INLG 2019: 529-539
2018
[c62]
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  - conf/inlg/ChenDL18
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Guanyi Chen, Kees van Deemter, Chenghua Lin:
SimpleNLG-ZH: a Linguistic Realisation Engine for Mandarin. INLG 2018: 57-66
[c61]
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  - conf/inlg/ChenDL18a
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Guanyi Chen, Kees van Deemter, Chenghua Lin:
Modelling Pro-drop with the Rational Speech Acts Model. INLG 2018: 159-164

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