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pro vyhledávání: '"Zhang, Yingji"'
Achieving precise semantic control over the latent spaces of Variational AutoEncoders (VAEs) holds significant value for downstream tasks in NLP as the underlying generative mechanisms could be better localised, explained and improved upon. Recent re
Externí odkaz:
http://arxiv.org/abs/2402.00723
Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-level latent spaces. To combine the controllability of VAE latent spaces
Externí odkaz:
http://arxiv.org/abs/2312.13208
The injection of syntactic information in Variational AutoEncoders (VAEs) has been shown to result in an overall improvement of performances and generalisation. An effective strategy to achieve such a goal is to separate the encoding of distributiona
Externí odkaz:
http://arxiv.org/abs/2311.08579
Explainable natural language inference aims to provide a mechanism to produce explanatory (abductive) inference chains which ground claims to their supporting premises. A recent corpus called EntailmentBank strives to advance this task by explaining
Externí odkaz:
http://arxiv.org/abs/2308.03581
Disentangled latent spaces usually have better semantic separability and geometrical properties, which leads to better interpretability and more controllable data generation. While this has been well investigated in Computer Vision, in tasks such as
Externí odkaz:
http://arxiv.org/abs/2305.01713
Formal/symbolic semantics can provide canonical, rigid controllability and interpretability to sentence representations due to their \textit{localisation} or \textit{composition} property. How can we deliver such property to the current distributiona
Externí odkaz:
http://arxiv.org/abs/2210.06230
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing. Currently, most disentanglement methods are unsupervised or rely on
Externí odkaz:
http://arxiv.org/abs/2210.02898
Autor:
Zhang, Yingji, Yao, Shanshan, Zhuang, Ruiyuan, Luan, Kaijun, Qian, Xinye, Xiang, Jun, Shen, Xiangqian, Li, Tianbao, Xiao, Kesong, Qin, Shibiao
Publikováno v:
In Journal of Alloys and Compounds 30 December 2017 729:1136-1144
Akademický článek
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Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control, and understanding downstream task performance in Natural Language Processing. The connection points between disentanglement and dow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43e48180ea6cdd3d247c5b448400063d