Label Attention Network for Structured Prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3APGQ5CQQE" target="_blank" >RIV/00216208:11320/22:PGQ5CQQE - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TASLP.2022.3145311" target="_blank" >https://doi.org/10.1109/TASLP.2022.3145311</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TASLP.2022.3145311" target="_blank" >10.1109/TASLP.2022.3145311</a>
Alternative languages
Result language
angličtina
Original language name
Label Attention Network for Structured Prediction
Original language description
Sequence labeling assigns a label to each token in a sequence, which is a fundamental problem in natural language processing (NLP). Many NLP tasks, including part-of-speech tagging and named entity recognition, can be solved in a form of sequence labeling problem. Other tasks such as constituency parsing and non-autoregressive machine translation can also be transformed into sequence labeling tasks. Neural models have been shown powerful for sequence labeling by employing a multi-layer sequence encoding network. Conditional random field (CRF) is proposed to enrich information over label sequences, yet it suffers large computational complexity and over-reliance on Marko assumption. To this end, we propose label attention network (LAN) to hierarchically refine representation of marginal label distributions bottom-up, enabling higher layers to learn more informed label sequence distribution based on information from lower layers. We demonstrate the effectiveness of LAN through extensive experiments on various NLP tasks including POS tagging, NER, CCG supertagging, constituency parsing and non-autoregressive machine translation. Empirical results show that LAN not only improves the overall tagging accuracy with similar number of parameters, but also significantly speeds up the training and testing compared to CRF.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE/ACM Transactions on Speech and Language Processing [online]
ISSN
2329-9304
e-ISSN
2329-9304
Volume of the periodical
30
Issue of the periodical within the volume
2022
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
1235-1248
UT code for WoS article
000777325700002
EID of the result in the Scopus database
2-s2.0-85124210855