Ryoma Yoshimura


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SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction
Ryoma Yoshimura | Masahiro Kaneko | Tomoyuki Kajiwara | Mamoru Komachi
Proceedings of the 28th International Conference on Computational Linguistics

We propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction (GEC). Previous studies have shown that reference-less metrics are promising; however, existing metrics are not optimized for manual evaluations of the system outputs because no dataset of the system output exists with manual evaluation. This study manually evaluates outputs of GEC systems to optimize the metrics. Experimental results show that the proposed metric improves correlation with the manual evaluation in both system- and sentence-level meta-evaluation. Our dataset and metric will be made publicly available.


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Filtering Pseudo-References by Paraphrasing for Automatic Evaluation of Machine Translation
Ryoma Yoshimura | Hiroki Shimanaka | Yukio Matsumura | Hayahide Yamagishi | Mamoru Komachi
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

In this paper, we introduce our participation in the WMT 2019 Metric Shared Task. We propose an improved version of sentence BLEU using filtered pseudo-references. We propose a method to filter pseudo-references by paraphrasing for automatic evaluation of machine translation (MT). We use the outputs of off-the-shelf MT systems as pseudo-references filtered by paraphrasing in addition to a single human reference (gold reference). We use BERT fine-tuned with paraphrase corpus to filter pseudo-references by checking the paraphrasability with the gold reference. Our experimental results of the WMT 2016 and 2017 datasets show that our method achieved higher correlation with human evaluation than the sentence BLEU (SentBLEU) baselines with a single reference and with unfiltered pseudo-references.