24 citations · 134 across the 37 of their papers we have counts for
39 papers
Tracking Temporal Dynamics of Vector Sets with Gaussian Process
Taichi Aida, Mamoru Komachi, Toshinobu Ogiso +2
Understanding the temporal evolution of sets of vectors is a fundamental challenge across various domains, including ecology, crime analysis, and linguistics. For instance, ecosyst…
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices
Hajime Kiyama, Taichi Aida, Mamoru Komachi +3
The meanings and relationships of words shift over time. This phenomenon is referred to as semantic shift. Research focused on understanding how semantic shifts occur over multiple…
Revisiting Meta-evaluation for Grammatical Error Correction
Masamune Kobayashi, Masato Mita, Mamoru Komachi
Metrics are the foundation for automatic evaluation in grammatical error correction (GEC), with their evaluation of the metrics (meta-evaluation) relying on their correlation with…
WikiSQE: A Large-Scale Dataset for Sentence Quality Estimation in Wikipedia
Kenichiro Ando, Satoshi Sekine, Mamoru Komachi
Wikipedia can be edited by anyone and thus contains various quality sentences. Therefore, Wikipedia includes some poor-quality edits, which are often marked up by other editors. Wh…
Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?
Kenichiro Ando, Mamoru Komachi, Takashi Okumura +2
During the patient's hospitalization, the physician must record daily observations of the patient and summarize them into a brief document called "discharge summary" when the patie…
Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan
Kenichiro Ando, Takashi Okumura, Mamoru Komachi +2
Automated summarization of clinical texts can reduce the burden of medical professionals. "Discharge summaries" are one promising application of the summarization, because they can…
Learning How to Translate North Korean through South Korean
Hwichan Kim, Sangwhan Moon, Naoaki Okazaki +1
South and North Korea both use the Korean language. However, Korean NLP research has focused on South Korean only, and existing NLP systems of the Korean language, such as neural m…
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction
Daisuke Suzuki, Yujin Takahashi, Ikumi Yamashita +5
In grammatical error correction (GEC), automatic evaluation is an important factor for research and development of GEC systems. Previous studies on automatic evaluation have demons…
Proficiency Matters Quality Estimation in Grammatical Error Correction
Yujin Takahashi, Masahiro Kaneko, Masato Mita +1
This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners' proficiency with the data. QE models for G…
Neural Combinatory Constituency Parsing
Zhousi Chen, Longtu Zhang, Aizhan Imankulova +1
We propose two fast neural combinatory models for constituency parsing: binary and multi-branching. Our models decompose the bottom-up parsing process into 1) classification of tag…
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding
Rob van der Goot, Ibrahim Sharaf, Aizhan Imankulova +6
The lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot f…
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation
Seiichiro Kondo, Kengo Hotate, Masahiro Kaneko +1
Neural machine translation (NMT) has recently gained widespread attention because of its high translation accuracy. However, it shows poor performance in the translation of long se…
Comparison of Grammatical Error Correction Using Back-Translation Models
Aomi Koyama, Kengo Hotate, Masahiro Kaneko +1
Grammatical error correction (GEC) suffers from a lack of sufficient parallel data. Therefore, GEC studies have developed various methods to generate pseudo data, which comprise pa…
Chinese Grammatical Correction Using BERT-based Pre-trained Model
Hongfei Wang, Michiki Kurosawa, Satoru Katsumata +1
In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization. In this study, we verify the effectiveness of…
JSSS: free Japanese speech corpus for summarization and simplification
Shinnosuke Takamichi, Mamoru Komachi, Naoko Tanji +1
In this paper, we construct a new Japanese speech corpus for speech-based summarization and simplification, "JSSS" (pronounced "j-triple-s"). Given the success of reading-style spe…
Keyframe Segmentation and Positional Encoding for Video-guided Machine Translation Challenge 2020
Tosho Hirasawa, Zhishen Yang, Mamoru Komachi +1
Video-guided machine translation as one of multimodal neural machine translation tasks targeting on generating high-quality text translation by tangibly engaging both video and tex…
Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model
Satoru Katsumata, Mamoru Komachi
Studies on grammatical error correction (GEC) have reported the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time…
Towards Multimodal Simultaneous Neural Machine Translation
Aizhan Imankulova, Masahiro Kaneko, Tosho Hirasawa +1
Simultaneous translation involves translating a sentence before the speaker's utterance is completed in order to realize real-time understanding in multiple languages. This task is…
Dynamic Fusion: Attentional Language Model for Neural Machine Translation
Michiki Kurosawa, Mamoru Komachi
Neural Machine Translation (NMT) can be used to generate fluent output. As such, language models have been investigated for incorporation with NMT. In prior investigations, two mod…
Improving Context-aware Neural Machine Translation with Target-side Context
Hayahide Yamagishi, Mamoru Komachi
In recent years, several studies on neural machine translation (NMT) have attempted to use document-level context by using a multi-encoder and two attention mechanisms to read the…
Machine Translation Evaluation with BERT Regressor
Hiroki Shimanaka, Tomoyuki Kajiwara, Mamoru Komachi
We introduce the metric using BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019) for automatic machine translation evaluation. The experimental re…
Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus
Satoru Katsumata, Mamoru Komachi
We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Goog…
Debiasing Word Embeddings Improves Multimodal Machine Translation
Tosho Hirasawa, Mamoru Komachi
In recent years, pretrained word embeddings have proved useful for multimodal neural machine translation (NMT) models to address the shortage of available datasets. However, the in…
Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection
Masahiro Kaneko, Mamoru Komachi
It is known that a deep neural network model pre-trained with large-scale data greatly improves the accuracy of various tasks, especially when there are resource constraints. Howev…