21 citations · 57 across the 16 of their papers we have counts for
4 papers · 2 filters
A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito +2
To promote and further develop RST-style discourse parsing models, we need a strong baseline that can be regarded as a reference for reporting reliable experimental results. This p…
Domain Adaptation of Machine Translation with Crowdworkers
Makoto Morishita, Jun Suzuki, Masaaki Nagata
Although a machine translation model trained with a large in-domain parallel corpus achieves remarkable results, it still works poorly when no in-domain data are available. This si…
Extending Word-Level Quality Estimation for Post-Editing Assistance
Yizhen Wei, Takehito Utsuro, Masaaki Nagata
We define a novel concept called extended word alignment in order to improve post-editing assistance efficiency. Based on extended word alignment, we further propose a novel task c…
JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus
Makoto Morishita, Katsuki Chousa, Jun Suzuki +1
Most current machine translation models are mainly trained with parallel corpora, and their translation accuracy largely depends on the quality and quantity of the corpora. Althoug…