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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)
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Simple Search
Hits 1 – 10 of 10
1
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition ...
Wang, Xinyu
;
Shen, Yongliang
;
Cai, Jiong
. - : arXiv, 2022
BASE
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2
Automated Concatenation of Embeddings for Structured Prediction ...
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
;
Wang, Xinyu
. - : Underline Science Inc., 2021
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3
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning ...
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
;
Wang, Xinyu
. - : Underline Science Inc., 2021
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4
Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings ...
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
;
Jia, Zixia
;
Wang, Xinyu
. - : Underline Science Inc., 2021
BASE
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5
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor ...
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
;
Jia, Zixia
;
Wang, Xinyu
;
Yan, Zhaohui
. - : Underline Science Inc., 2021
Abstract:
Read paper: https://www.aclanthology.org/2021.acl-long.46 Abstract: Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions. However, for structured prediction problems, the output space is exponential in size; therefore, the cross-entropy objective becomes intractable to compute and optimize directly. In this paper, we derive a factorized form of the knowledge distillation objective for structured prediction, which is tractable for many typical choices of the teacher and student models. In particular, we show the tractability and empirical effectiveness of structural knowledge distillation between sequence labeling and dependency parsing models under four different scenarios: 1) the teacher and student share the same factorization form of the output structure ...
Keyword:
Computational Linguistics
;
Condensed Matter Physics
;
Deep Learning
;
Electromagnetism
;
FOS Physical sciences
;
Information and Knowledge Engineering
;
Neural Network
;
Semantics
URL:
https://underline.io/lecture/25403-structural-knowledge-distillation-tractably-distilling-information-for-structured-predictor
https://dx.doi.org/10.48448/jyb5-ce02
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6
Dependency Induction Through the Lens of Visual Perception ...
Su, Ruisi
;
Rijhwani, Shruti
;
Zhu, Hao
. - : arXiv, 2021
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7
Dependency Induction Through the Lens of Visual Perception ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Hao
;
Bisk, Yonatan
. - : Underline Science Inc., 2021
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8
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering ...
Wang, Xinyu
;
Liu, Yuliang
;
Shen, Chunhua
. - : arXiv, 2020
BASE
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9
Structure-Level Knowledge Distillation For Multilingual Sequence Labeling ...
Wang, Xinyu
;
Jiang, Yong
;
Bach, Nguyen
. - : arXiv, 2020
BASE
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10
A novel stock recommendation system using Guba sentiment analysis [<Journal>]
Sun, Yunchuan
[Verfasser];
Fang, Mengting
[Sonstige];
Wang, Xinyu
[Sonstige]
DNB Subject Category Language
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