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Hits 1 – 20 of 28
1
How does the pre-training objective affect what large language models learn about linguistic properties? ...
Alajrami, Ahmed
;
Aletras, Nikolaos
. - : arXiv, 2022
BASE
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2
Automatic Identification and Classification of Bragging in Social Media ...
Jin, Mali
;
Preoţiuc-Pietro, Daniel
;
Doğruöz, A. Seza
. - : arXiv, 2022
BASE
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3
Analyzing Online Political Advertisements ...
Villegas, Danae Sánchez
;
Mokaram, Saeid
;
Aletras, Nikolaos
. - : arXiv, 2021
BASE
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4
Modeling the Severity of Complaints in Social Media ...
Jin, Mali
;
Aletras, Nikolaos
. - : arXiv, 2021
BASE
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5
Translation Error Detection as Rationale Extraction ...
Fomicheva, Marina
;
Specia, Lucia
;
Aletras, Nikolaos
. - : arXiv, 2021
BASE
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6
Knowledge Distillation for Quality Estimation ...
Gajbhiye, Amit
;
Fomicheva, Marina
;
Alva-Manchego, Fernando
. - : arXiv, 2021
BASE
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7
Frustratingly Simple Pretraining Alternatives to Masked Language Modeling ...
Yamaguchi, Atsuki
;
Chrysostomou, George
;
Margatina, Katerina
. - : arXiv, 2021
BASE
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8
Analyzing Online Political Advertisements ...
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
;
Aletras, Nikolaos
;
Mokaram, Saeid
. - : Underline Science Inc., 2021
BASE
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9
Improving the Faithfulness of Attention-based Explanations with Task-specific Information for Text Classification ...
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
;
Aletras, Nikolaos
;
Chrysostomou, George
. - : Underline Science Inc., 2021
BASE
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10
Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Aletras, Nikolaos
;
Chrysostomou, George
. - : Underline Science Inc., 2021
BASE
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11
Modeling the Severity of Complaints in Social Media ...
NAACL 2021 2021
;
Aletras, Nikolaos
;
Jin, Mali
. - : Underline Science Inc., 2021
BASE
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12
Active Learning by Acquiring Contrastive Examples ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Aletras, Nikolaos
;
Barrault, Loïc
. - : Underline Science Inc., 2021
BASE
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13
In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering ...
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
;
Aletras, Nikolaos
;
Barrault, Loïc
. - : Underline Science Inc., 2021
BASE
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14
Frustratingly Simple Pretraining Alternatives to Masked Language Modeling ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Aletras, Nikolaos
;
Chrysostomou, George
. - : Underline Science Inc., 2021
BASE
Show details
15
Knowledge Distillation for Quality Estimation ...
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
;
Aletras, Nikolaos
;
Alva-Manchego, Fernando
;
Blain, Frédéric
;
Fomicheva, Marina
;
Gajbhiye, Amit
;
Obamuyide, Abiola
;
Specia, Lucia
. - : Underline Science Inc., 2021
Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.452 Abstract: Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media conversations. Recent success in QE stems from the use of multilingual pre-trained representations, where very large models lead to impressive results. However, the inference time, disk and memory requirements of such models do not allow for wide usage in the real world. Models trained on distilled pre-trained representations remain prohibitively large for many usage scenarios. We instead propose to directly transfer knowledge from a strong QE teacher model to a much smaller model with a different, shallower architecture. We show that this approach, in combination with data augmentation, leads to light-weight QE models that perform competitively with distilled pre-trained representations with 8x fewer parameters. ...
Keyword:
Computational Linguistics
;
Condensed Matter Physics
;
Deep Learning
;
Electromagnetism
;
FOS Physical sciences
;
Information and Knowledge Engineering
;
Neural Network
;
Semantics
URL:
https://underline.io/lecture/26543-knowledge-distillation-for-quality-estimation
https://dx.doi.org/10.48448/cc9y-3821
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16
Machine Extraction of Tax Laws from Legislative Texts
Han, Luyang
;
Guillot, Malka
;
Ash, Elliott
In: Proceedings of the Natural Legal Language Processing Workshop 2021 (2021)
BASE
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17
Point-of-Interest Type Prediction using Text and Images ...
Villegas, Danae Sánchez
;
Aletras, Nikolaos
. - : arXiv, 2021
BASE
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18
Point-of-Interest Type Prediction using Text and Images ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Aletras, Nikolaos
;
Sanchez Villegas, Danae
. - : Underline Science Inc., 2021
BASE
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19
An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Aletras, Nikolaos
;
Mensah, Samuel
. - : Underline Science Inc., 2021
BASE
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20
Knowledge distillation for quality estimation
Gajbhiye, Amit
;
Fomicheva, Marina
;
Alva-Manchego, Fernando
. - : Association for Computational Linguistics, 2021
BASE
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