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1
On Homophony and Rényi Entropy ...
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2
An Information-Theoretic Characterization of Morphological Fusion ...
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3
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders ...
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4
Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution ...
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5
Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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6
Visually Grounded Reasoning across Languages and Cultures ...
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7
I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Review ...
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8
MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer ...
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9
IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages ...
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10
Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study ...
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11
Mitigating Language-Dependent Ethnic Bias in BERT ...
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12
Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
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13
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ...
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14
Recent Advances in Dialogue Machine Translation
In: Information ; Volume 12 ; Issue 11 (2021)
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15
Visually Grounded Reasoning across Languages and Cultures ...
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16
Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.558/ Abstract: While neural networks produce state-of-the-art performance in several NLP tasks, they depend heavily on lexicalized information, which transfers poorly between domains. Previous work proposed delexicalization as a form of knowledge distillation to reduce dependency on such lexical artifacts. However, a critical unsolved issue that remains is how much delexicalization should be applied? A little helps reduce over-fitting, but too much discards useful information. We propose Group Learning (GL), a knowledge and model distillation approach for fact verification. In our method, while multiple student models have access to different delexicalized data views, they are encouraged to independently learn from each other through pair-wise consistency losses. In several cross-domain experiments between the FEVER and FNC fact verification datasets, we show that our approach learns the best delexicalization strategy for the given training ...
Keyword: Data Management System; Machine Learning; Machine translation; Natural Language Processing
URL: https://dx.doi.org/10.48448/cfrk-4935
https://underline.io/lecture/37416-students-who-study-together-learn-better-on-the-importance-of-collective-knowledge-distillation-for-domain-transfer-in-fact-verification
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17
On the Relation between Syntactic Divergence and Zero-Shot Performance ...
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18
Bridging the “gApp”: improving neural machine translation systems for multiword expression detection
In: 11 ; 1 ; 61 ; 80 (2020)
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19
Translating ontologies in real-world settings
Arcan, Mihael; Dragoni, Mauro; Buitelaar, Paul. - : Springer Verlag, 2019
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20
PADIC: extension and new experiments
In: 7th International Conference on Advanced Technologies ; 7th International Conference on Advanced Technologies ICAT ; https://hal.archives-ouvertes.fr/hal-01718858 ; 7th International Conference on Advanced Technologies ICAT, Apr 2018, Antalya, Turkey (2018)
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