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Hits 1 – 20 of 49
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
BASE
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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
Show details
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
BASE
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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
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20
BURT: BERT-inspired Universal Representation from Twin Structure ...
Li, Yian
;
Zhao, Hai
. - : arXiv, 2020
Abstract:
Pre-trained contextualized language models such as BERT have shown great effectiveness in a wide range of downstream Natural Language Processing (NLP) tasks. However, the effective representations offered by the models target at each token inside a sequence rather than each sequence and the fine-tuning step involves the input of both sequences at one time, leading to unsatisfying representations of various sequences with different granularities. Especially, as sentence-level representations taken as the full training context in these models, there comes inferior performance on lower-level linguistic units (phrases and words). In this work, we present BURT (BERT inspired Universal Representation from Twin Structure) that is capable of generating universal, fixed-size representations for input sequences of any granularity, i.e., words, phrases, and sentences, using a large scale of natural language inference and paraphrase data with multiple training objectives. Our proposed BURT adopts the Siamese network, ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://arxiv.org/abs/2004.13947
https://dx.doi.org/10.48550/arxiv.2004.13947
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