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Søgaard, Anders (12)
The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (8)
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 (4)
Aralikatte, Rahul (2)
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2021 (12)
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Simple Search
Hits 1 – 12 of 12
1
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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
;
Aralikatte, Rahul
;
Maria Ponti, Edoardo
. - : Underline Science Inc., 2021
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2
Evaluation of Summarization Systems across Gender, Age, and Race ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Jørgensen, Anna
;
Søgaard, Anders
. - : Underline Science Inc., 2021
BASE
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3
Locke's Holiday: Belief Bias in Machine Reading ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Søgaard, Anders
. - : Underline Science Inc., 2021
BASE
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4
Dynamic Forecasting of Conversation Derailment ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Kementchedjhieva, Yova
;
Søgaard, Anders
. - : Underline Science Inc., 2021
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5
Replicating and Extending ``Because Their Treebanks Leak'': Graph Isomorphism, Covariants, and Parser Performance ...
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
;
Anderson, Mark
;
Gómez-Rodríguez, Carlos
. - : Underline Science Inc., 2021
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6
Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Abdou, Mostafa
;
Frank, Stella
. - : Underline Science Inc., 2021
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7
Spurious Correlations in Cross-Topic Argument Mining ...
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
;
Barrett, Maria
;
Søgaard, Anders
. - : Underline Science Inc., 2021
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8
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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
;
Aralikatte, Rahul
;
Maria Ponti, Edoardo
. - : Underline Science Inc., 2021
BASE
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9
A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
de Lhoneux, Miryam
;
Hartmann, Mareike
. - : Underline Science Inc., 2021
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10
Clustering Monolingual Vocabularies to Improve Cross-Lingual Generalization ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Tejaswini
;
Bassani, Riccardo
. - : Underline Science Inc., 2021
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11
The Impact of Positional Encodings on Multilingual Compression ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Ravishankar, Vinit
;
Søgaard, Anders
. - : Underline Science Inc., 2021
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12
On Language Models for Creoles ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Miryam
;
Bugliarello, Emanuele
;
Lent, Heather
;
Qui, Chen
;
Søgaard, Anders
. - : Underline Science Inc., 2021
Abstract:
Creole languages such as Nigerian Pidgin English and Haitian Creole are under-resourced and largely ignored in the NLP literature. Creoles typically result from the fusion of a foreign language with multiple local languages, and what grammatical and lexical features are transferred to the creole is a complex process. While creoles are generally stable, the prominence of some features may be much stronger with certain demographics or in some linguistic situations. This paper makes several contributions: We collect existing corpora and release models for Haitian Creole, Nigerian Pidgin English, and Singaporean Colloquial English. We evaluate these models on intrinsic and extrinsic tasks. Motivated by the above literature, we compare standard language models with distributionally robust ones and find that, somewhat surprisingly, the standard language models are superior to the distributionally robust ones. We investigate whether this is an effect of over-parameterization or relative distributional stability, ...
Keyword:
Computational Linguistics
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
URL:
https://underline.io/lecture/39854-on-language-models-for-creoles
https://dx.doi.org/10.48448/5t1r-xn60
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