Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Li, Shu'ang"'
How to identify semantic relations among entities in a document when only a few labeled documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial for addressing the pervasive data scarcity problem in real-world scenario
Externí odkaz:
http://arxiv.org/abs/2310.15743
Recently, text watermarking algorithms for large language models (LLMs) have been proposed to mitigate the potential harms of text generated by LLMs, including fake news and copyright issues. However, current watermark detection algorithms require th
Externí odkaz:
http://arxiv.org/abs/2307.16230
Named Entity Recognition (NER) is a well and widely studied task in natural language processing. Recently, the nested NER has attracted more attention since its practicality and difficulty. Existing works for nested NER ignore the recognition order a
Externí odkaz:
http://arxiv.org/abs/2305.07266
Autor:
Liu, Aiwei, Yu, Honghai, Hu, Xuming, Li, Shu'ang, Lin, Li, Ma, Fukun, Yang, Yawen, Wen, Lijie
Publikováno v:
EMNLP 2022
We propose the first character-level white-box adversarial attack method against transformer models. The intuition of our method comes from the observation that words are split into subtokens before being fed into the transformer models and the subst
Externí odkaz:
http://arxiv.org/abs/2210.17004
Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and hypothesis). Recently, low-resource natural language inference has gai
Externí odkaz:
http://arxiv.org/abs/2205.15550
Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer the relationship between the sentence pair (premise and hypothesis). Many recent works have used contrastive learning b
Externí odkaz:
http://arxiv.org/abs/2201.10927
Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challenging task in event sequence prediction because it requires not only fluen
Externí odkaz:
http://arxiv.org/abs/2201.07099
Akademický článek
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Publikováno v:
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challenging task in event sequence prediction because it requires not only fluen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e433c4907ca7ea8e77dcc7c673ffe5c2
https://doi.org/10.1145/3477495.3532080
https://doi.org/10.1145/3477495.3532080
Autor:
Qian, Chen, Wen, Lijie, Kumar, Akhil, Lin, Leilei, Lin, Li, Zong, Zan, Li, Shu’ang, Wang, Jianmin
Publikováno v:
Advanced Information Systems Engineering
Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions. Previous process extractors heavily
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25dccb2a15bd28b0abcd60308204958b
http://arxiv.org/abs/1906.02127
http://arxiv.org/abs/1906.02127