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Hits 1 – 20 of 52
1
Semantics-Preserved Distortion for Personal Privacy Protection ...
Peng, Letian
;
Li, Zuchao
;
Zhao, Hai
. - : arXiv, 2022
BASE
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2
Pre-training Universal Language Representation ...
Li, Yian
;
Zhao, Hai
. - : arXiv, 2021
BASE
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3
Head-driven Phrase Structure Parsing in O($n^3$) Time Complexity ...
Li, Zuchao
;
Zhou, Junru
;
Zhao, Hai
. - : arXiv, 2021
BASE
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4
Multi-tasking Dialogue Comprehension with Discourse Parsing ...
He, Yuchen
;
Zhang, Zhuosheng
;
Zhao, Hai
. - : arXiv, 2021
BASE
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5
Structural Pre-training for Dialogue Comprehension ...
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
;
Zhang, Zhuosheng
;
Zhao, Hai
. - : Underline Science Inc., 2021
BASE
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6
Smoothing Dialogue States for Open Conversational Machine Reading ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Ouyang, Siru
;
Zhang, Zhuosheng
. - : Underline Science Inc., 2021
BASE
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7
Dialogue Graph Modeling for Conversational Machine Reading ...
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
;
Ouyang, Siru
;
Zhang, Zhuosheng
. - : Underline Science Inc., 2021
BASE
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8
Dialogue-oriented Pre-training ...
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
;
Xu, Yi
;
Zhao, Hai
. - : Underline Science Inc., 2021
Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.235 Abstract: Pre-trained language models (PrLM) has been shown powerful in enhancing a broad range of downstream tasks including various dialogue related ones. However, PrLMs are usually trained on general plain text with common language model (LM) training objectives, which cannot sufficiently capture dialogue exclusive features due to the limitation of such training setting, so that there is an immediate need to fill the gap between a specific dialogue task and the LM task. As it is unlikely to collect huge dialogue data for dialogue-oriented pre-training, in this paper, we propose three strategies to simulate the conversation features on general plain text. Our proposed method differs from existing post-training methods that it may yield a general-purpose PrLM and does not individualize to any detailed task while keeping the capability of learning dialogue related features including speaker awareness, continuity and consistency. The resulted ...
Keyword:
Computational Linguistics
;
Condensed Matter Physics
;
Deep Learning
;
Electromagnetism
;
FOS Physical sciences
;
Information and Knowledge Engineering
;
Neural Network
;
Semantics
URL:
https://dx.doi.org/10.48448/frsn-n538
https://underline.io/lecture/26326-dialogue-oriented-pre-training
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9
Pre-training Universal Language Representation ...
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
;
Li, Yian
;
Zhao, Hai
. - : Underline Science Inc., 2021
BASE
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10
Unsupervised Neural Machine Translation with Universal Grammar ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Li, Zuchao
;
Sumita, Eiichiro
. - : Underline Science Inc., 2021
BASE
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11
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Jiang, Shu
;
Jing, Hongjiang
. - : Underline Science Inc., 2021
BASE
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12
Enhancing Language Generation with Effective Checkpoints of Pre-trained Language Model ...
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
;
Park, Jeonghyeok
;
Zhao, Hai
. - : Underline Science Inc., 2021
BASE
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13
Syntax Role for Neural Semantic Role Labeling ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Cai, Jiaxun
;
He, Shexia
. - : Underline Science Inc., 2021
BASE
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14
Tracing Origins: Coreference-aware Machine Reading Comprehension ...
Huang, Baorong
;
Zhang, Zhuosheng
;
Zhao, Hai
. - : arXiv, 2021
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15
Syntax-aware Data Augmentation for Neural Machine Translation ...
Duan, Sufeng
;
Zhao, Hai
;
Zhang, Dongdong
. - : arXiv, 2020
BASE
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16
Cross-lingual Supervision Improves Unsupervised Neural Machine Translation ...
Wang, Mingxuan
;
Bai, Hongxiao
;
Zhao, Hai
. - : arXiv, 2020
BASE
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17
Syntax Role for Neural Semantic Role Labeling ...
Li, Zuchao
;
Zhao, Hai
;
He, Shexia
. - : arXiv, 2020
BASE
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18
BURT: BERT-inspired Universal Representation from Learning Meaningful Segment ...
Li, Yian
;
Zhao, Hai
. - : arXiv, 2020
BASE
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19
Learning Universal Representations from Word to Sentence ...
Li, Yian
;
Zhao, Hai
. - : arXiv, 2020
BASE
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20
BURT: BERT-inspired Universal Representation from Twin Structure ...
Li, Yian
;
Zhao, Hai
. - : arXiv, 2020
BASE
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