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20182023
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cs.CL2023

Unbalanced Optimal Transport for Unbalanced Word Alignment

Yuki Arase, Han Bao, Sho Yokoi

Monolingual word alignment is crucial to model semantic interactions between sentences. In particular, null alignment, a phenomenon in which words have no corresponding counterpart…

cs.CL2023

Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research

Ji-Ung Lee, Haritz Puerto, Betty van Aken +8

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 cos…

cs.CL2022

CEFR-Based Sentence Difficulty Annotation and Assessment

Yuki Arase, Satoru Uchida, Tomoyuki Kajiwara

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 a…

cs.CL2020

A Corpus for English-Japanese Multimodal Neural Machine Translation with Comparable Sentences

Andrew Merritt, Chenhui Chu, Yuki Arase

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…

cs.CL2020

Lexically Cohesive Neural Machine Translation with Copy Mechanism

Vipul Mishra, Chenhui Chu, Yuki Arase

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…

cs.CL2018

Recursive Neural Network Based Preordering for English-to-Japanese Machine Translation

Yuki Kawara, Chenhui Chu, Yuki Arase

The word order between source and target languages significantly influences the translation quality in machine translation. Preordering can effectively address this problem. Previo…