| Pre-Trained Word Embedding and Language Model Improve Multimodal Machine Translation: A Case Study in Multi30K | 0 | 0.34 | 2022 |
| Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation | 0 | 0.34 | 2022 |
| TMU NMT System with Automatic Post-Editing by Multi-Source Levenshtein Transformer for the Restricted Translation Task of WAT 2022. | 0 | 0.34 | 2022 |
| Region-attentive multimodal neural machine translation | 0 | 0.34 | 2022 |
| Why Videos Do Not Guide Translations in Video-guided Machine Translation? An Empirical Evaluation of Video-guided Machine Translation Dataset. | 0 | 0.34 | 2022 |
| Machine Translation With Pre-Specified Target-Side Words Using A Semi-Autoregressive Model | 0 | 0.34 | 2021 |
| Tmeku System For The Wat2021 Multimodal Translation Task | 0 | 0.34 | 2021 |
| Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings. | 0 | 0.34 | 2021 |
| From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding | 0 | 0.34 | 2021 |
| Neural Combinatory Constituency Parsing. | 0 | 0.34 | 2021 |
| Using Sub-character Level Information for Neural Machine Translation of Logographic Languages | 0 | 0.34 | 2021 |
| Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation | 1 | 0.41 | 2021 |
| Comparison of Grammatical Error Correction Using Back-Translation Models | 0 | 0.34 | 2021 |
| Cross-lingual Transfer Learning for Grammatical Error Correction. | 0 | 0.34 | 2020 |
| Chinese Grammatical Correction Using BERT-based Pre-trained Model | 0 | 0.34 | 2020 |
| Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of Japanese as a Second Language. | 0 | 0.34 | 2020 |
| Double Attention-based Multimodal Neural Machine Translation with Semantic Image Regions. | 0 | 0.34 | 2020 |
| Zero-Shot North Korean To English Neural Machine Translation By Character Tokenization And Phoneme Decomposition | 0 | 0.34 | 2020 |
| TMU Japanese-English Multimodal Machine Translation System for WAT 2020. | 0 | 0.34 | 2020 |
| Automated Essay Scoring System for Nonnative Japanese Learners. | 0 | 0.34 | 2020 |
| English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs | 0 | 0.34 | 2020 |
| Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner's Error Tendency. | 0 | 0.34 | 2020 |
| TMUOU Submission for WMT20 Quality Estimation Shared Task. | 0 | 0.34 | 2020 |
| Translation of New Named Entities from English to Chinese. | 0 | 0.34 | 2020 |
| Towards Multimodal Simultaneous Neural Machine Translation | 0 | 0.34 | 2020 |
| Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model | 0 | 0.34 | 2020 |
| SOME - Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction. | 0 | 0.34 | 2020 |
| Korean-to-Japanese Neural Machine Translation System using Hanja Information. | 0 | 0.34 | 2020 |
| Multi-task Learning for Japanese Predicate Argument Structure Analysis. | 0 | 0.34 | 2019 |
| Filtering Pseudo-References by Paraphrasing for Automatic Evaluation of Machine Translation | 1 | 0.35 | 2019 |
| Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019. | 0 | 0.34 | 2019 |
| Dynamic Fusion: Attentional Language Model for Neural Machine Translation | 0 | 0.34 | 2019 |
| Controlling Grammatical Error Correction Using Word Edit Rate | 0 | 0.34 | 2019 |
| Japanese Predicate Argument Structure Analysis with Pointer Networks. | 0 | 0.34 | 2019 |
| Grammatical-Error-Aware Incorrect Example Retrieval System For Learners Of Japanese As A Second Language | 0 | 0.34 | 2019 |
| Debiasing Word Embeddings Improves Multimodal Machine Translation. | 0 | 0.34 | 2019 |
| Multi-Head Multi-Layer Attention To Deep Language Representations For Grammatical Error Detection | 0 | 0.34 | 2019 |
| Tmu Transformer System Using Bert For Re-Ranking At Bea 2019 Grammatical Error Correction On Restricted Track | 0 | 0.34 | 2019 |
| Sakura: Large-Scale Incorrect Example Retrieval System For Learners Of Japanese As A Second Language | 0 | 0.34 | 2019 |
| (Almost) Unsupervised Grammatical Error Correction Using A Synthetic Comparable Corpus | 0 | 0.34 | 2019 |
| Improving Context-aware Neural Machine Translation with Target-side Context | 1 | 0.34 | 2019 |
| The Rule of Three: Abstractive Text Summarization in Three Bullet Points. | 0 | 0.34 | 2018 |
| TMU System for SLAM-2018. | 0 | 0.34 | 2018 |
| Complex Word Identification Based on Frequency in a Learner Corpus. | 0 | 0.34 | 2018 |
| Neural Machine Translation of Logographic Languages Using Sub-character Level Information. | 2 | 0.38 | 2018 |
| TMU Japanese-Chinese Unsupervised NMT System for WAT 2018 Translation Task. | 0 | 0.34 | 2018 |
| Construction of a Japanese Word Similarity Dataset. | 2 | 0.38 | 2018 |
| Japanese Predicate Conjugation for Neural Machine Translation. | 0 | 0.34 | 2018 |
| TMU Japanese-English Neural Machine Translation System using Generative Adversarial Network for WAT 2018. | 0 | 0.34 | 2018 |
| Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models. | 0 | 0.34 | 2018 |
