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pro vyhledávání: '"Arase, Yuki"'
We propose edit operation based lexically constrained decoding for sentence simplification. In sentence simplification, lexical paraphrasing is one of the primary procedures for rewriting complex sentences into simpler correspondences. While previous
Externí odkaz:
http://arxiv.org/abs/2409.19247
Autor:
Arase, Yuki, Kajiwara, Tomoyuki
In this study, we propose a method that distils representations of word meaning in context from a pre-trained masked language model in both monolingual and crosslingual settings. Word representations are the basis for context-aware lexical semantics
Externí odkaz:
http://arxiv.org/abs/2409.08719
Autor:
Wu, Xuanxin, Arase, Yuki
Sentence simplification, which rewrites a sentence to be easier to read and understand, is a promising technique to help people with various reading difficulties. With the rise of advanced large language models (LLMs), evaluating their performance in
Externí odkaz:
http://arxiv.org/abs/2403.04963
Autor:
Lee, Ji-Ung, Puerto, Haritz, van Aken, Betty, Arase, Yuki, Forde, Jessica Zosa, Derczynski, Leon, Rücklé, Andreas, Gurevych, Iryna, Schwartz, Roy, Strubell, Emma, Dodge, Jesse
Many recent improvements in NLP stem from the development and use of large pre-trained language models (PLMs) with billions of parameters. Large model sizes makes computational cost one of the main limiting factors for training and evaluating such mo
Externí odkaz:
http://arxiv.org/abs/2306.16900
Monolingual word alignment is crucial to model semantic interactions between sentences. In particular, null alignment, a phenomenon in which words have no corresponding counterparts, is pervasive and critical in handling semantically divergent senten
Externí odkaz:
http://arxiv.org/abs/2306.04116
Controllable text simplification is a crucial assistive technique for language learning and teaching. One of the primary factors hindering its advancement is the lack of a corpus annotated with sentence difficulty levels based on language ability des
Externí odkaz:
http://arxiv.org/abs/2210.11766
Autor:
Sasaki, Yuya, Hori, Keizo, Nishihara, Daiki, Ohashi, Sora, Wakuta, Yusuke, Harada, Kei, Onizuka, Makoto, Arase, Yuki, Shimojo, Shinji, Doi, Kenji, Hongdi, He, Peng, Zhong-Ren
Publikováno v:
EDBT 2021
Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute
Externí odkaz:
http://arxiv.org/abs/2104.06701
Multimodal neural machine translation (NMT) has become an increasingly important area of research over the years because additional modalities, such as image data, can provide more context to textual data. Furthermore, the viability of training multi
Externí odkaz:
http://arxiv.org/abs/2010.08725
Lexically cohesive translations preserve consistency in word choices in document-level translation. We employ a copy mechanism into a context-aware neural machine translation model to allow copying words from previous translation outputs. Different f
Externí odkaz:
http://arxiv.org/abs/2010.05193
Autor:
Arase, Yuki, Tsujii, Junichi
A semantic equivalence assessment is defined as a task that assesses semantic equivalence in a sentence pair by binary judgment (i.e., paraphrase identification) or grading (i.e., semantic textual similarity measurement). It constitutes a set of task
Externí odkaz:
http://arxiv.org/abs/1909.00931