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Interpreting Arabic Transformer Models ...
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Findings of the IWSLT 2020 Evaluation campaign
Niehues, Jan; Federico, Marcello; Ma, Xutai. - : Association for Computational Linguistics, 2022
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What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis ...
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4
How transfer learning impacts linguistic knowledge in deep NLP models? ...
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5
How transfer learning impacts linguistic knowledge in deep NLP models? ...
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Effect of Post-processing on Contextualized Word Representations ...
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Similarity Analysis of Contextual Word Representation Models ...
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8
Findings of the IWSLT 2020 Evaluation campaign ...
Ansari, Ebrahim; Axelrod, Amittai; Bach, Nguyen. - : Association for Computational Linguistics, 2020
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9
AraBench: Benchmarking Dialectal Arabic-English Machine Translation ...
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10
Analyzing Individual Neurons in Pre-trained Language Models ...
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11
On the Linguistic Representational Power of Neural Machine Translation Models
In: Computational Linguistics, Vol 46, Iss 1, Pp 1-52 (2020) (2020)
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12
On the Linguistic Representational Power of Neural Machine Translation Models ...
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13
What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models ...
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14
Identifying and Controlling Important Neurons in Neural Machine Translation ...
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15
Continuous Space Reordering Models for Phrase-based MT ...
Durrani, Nadir; Dalvi, Fahim. - : arXiv, 2018
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16
The Summa Platform Prototype ...
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17
The Summa Platform Prototype ...
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18
Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging ...
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19
Machine Translation Approaches and Survey for Indian Languages ...
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20
The SUMMA Platform Prototype
In: http://infoscience.epfl.ch/record/233575 (2017)
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