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Simple Search
Hits 1 – 12 of 12
1
Unsupervised Translation of German--Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language ...
Edman, Lukas
;
Üstün, Ahmet
;
Toral, Antonio
. - : arXiv, 2021
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2
On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions ...
van der Goot, Rob
;
Üstün, Ahmet
;
Plank, Barbara
. - : arXiv, 2021
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3
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters ...
Üstün, Ahmet
;
Bérard, Alexandre
;
Besacier, Laurent
. - : arXiv, 2021
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4
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Berard, Alexandre
;
Besacier, Laurent
. - : Underline Science Inc., 2021
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5
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ...
van der Goot, Rob
;
Sharaf, Ibrahim
;
Imankulova, Aizhan
. - : arXiv, 2021
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6
On the Difficulty of Translating Free-Order Case-Marking Languages ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bisazza, Arianna
;
Sportel, Stephan
. - : Underline Science Inc., 2021
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7
UDapter: Language Adaptation for Truly Universal Dependency Parsing ...
Üstün, Ahmet
;
Bisazza, Arianna
;
Bouma, Gosse
. - : arXiv, 2020
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8
FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings ...
Braaksma, Bertelt
;
Scholtens, Richard
;
van Suijlekom, Stan
. - : arXiv, 2020
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9
Incorporating word embeddings in unsupervised morphological segmentation
Üstün, Ahmet
;
Can, Burcu
In: 2020 ; 1 ; 21 (2020)
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10
Characters or morphemes: how to represent words?
Üstün, Ahmet
;
Kurfalı, Murathan
;
Can, Burcu
. - : Association for Computational Linguistics, 2018
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11
A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation ...
Kurfalı, Murathan
;
Üstün, Ahmet
;
Can, Burcu
. - : arXiv, 2017
Abstract:
In this paper, we introduce a trie-structured Bayesian model for unsupervised morphological segmentation. We adopt prior information from different sources in the model. We use neural word embeddings to discover words that are morphologically derived from each other and thereby that are semantically similar. We use letter successor variety counts obtained from tries that are built by neural word embeddings. Our results show that using different information sources such as neural word embeddings and letter successor variety as prior information improves morphological segmentation in a Bayesian model. Our model outperforms other unsupervised morphological segmentation models on Turkish and gives promising results on English and German for scarce resources. ... : 12 pages, accepted and presented at the CICLING 2017 - 18th International Conference on Intelligent Text Processing and Computational Linguistics ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://arxiv.org/abs/1704.07329
https://dx.doi.org/10.48550/arxiv.1704.07329
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12
Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small Datasets ...
Can, Burcu
;
Üstün, Ahmet
;
Kurfalı, Murathan
. - : arXiv, 2017
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