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Found 107 papers, 23 papers with code
UniDic for Early Middle Japanese: a Dictionary for Morphological Analysis of Classical Japanese
In order to construct an annotated diachronic corpus of Japanese, we propose to create a new dictionary for morphological analysis of Early Middle Japanese (Classical Japanese) based on UniDic, a dictionary for Contemporary Japanese.
Analysis of English Spelling Errors in a Word-Typing Game
Therefore, we propose a new correctable word-typing game that is more similar to the actual writing process.
Neural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation
This paper presents an improved lexicalized reordering model for phrase-based statistical machine translation using a deep neural network.
Japanese-English Machine Translation of Recipe Texts
However, little work has been done on machine translation of recipe texts.
Cultural Vocal Bursts Intensity Prediction
Information Retrieval
+2
Disaster Analysis using User-Generated Weather Report
Information extraction from user-generated text has gained much attention with the growth of the Web. Disaster analysis using information from social media provides valuable, real-time, geolocation information for helping people caught up these in disasters.
Building a Monolingual Parallel Corpus for Text Simplification Using Sentence Similarity Based on Alignment between Word Embeddings
To obviate the need for human annotation, we propose an unsupervised method that automatically builds the monolingual parallel corpus for text simplification using sentence similarity based on word embeddings.
Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation
The results showed that, we could control the voice of the generated sentence with 85. 0{\%} accuracy on average.
Sparse Named Entity Classification using Factorization Machines
Named entity classification is the task of classifying text-based elements into various categories, including places, names, dates, times, and monetary values.
Construction of a Japanese Word Similarity Dataset
An evaluation of distributed word representation is generally conducted using a word similarity task and/or a word analogy task.
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings
One of the most important problems in machine translation (MT) evaluation is to evaluate the similarity between translation hypotheses with different surface forms from the reference, especially at the segment level.
Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention
Previous approaches to training syntax-based sentiment classification models required phrase-level annotated corpora, which are not readily available in many languages other than English.
English-Japanese Neural Machine Translation with Encoder-Decoder-Reconstructor
We confirm that our re-implementation also shows the same tendency and alleviates the problem of repeating and missing words in the translation on a English-Japanese task too.
Long Short-Term Memory for Japanese Word Segmentation
This study presents a Long Short-Term Memory (LSTM) neural network approach to Japanese word segmentation (JWS).
Ranked #3 on
Japanese Word Segmentation
on BCCWJ
MIPA: Mutual Information Based Paraphrase Acquisition via Bilingual Pivoting
We present a pointwise mutual information (PMI)-based approach to formalize paraphrasability and propose a variant of PMI, called MIPA, for the paraphrase acquisition.
Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings
In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns.
Ranked #6 on
Grammatical Error Detection
on FCE
Improving Japanese-to-English Neural Machine Translation by Voice Prediction
This study reports an attempt to predict the voice of reference using the information from the input sentences or previous input/output sentences.
Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language
Neural machine translation (NMT) produces sentences that are more fluent than those produced by statistical machine translation (SMT).
Tokyo Metropolitan University Neural Machine Translation System for WAT 2017
We implemented beam search and ensemble decoding in the NMT system.
Improving Low-Resource Neural Machine Translation with Filtered Pseudo-Parallel Corpus
Large-scale parallel corpora are indispensable to train highly accurate machine translators.
Language Modelling
Low-Resource Neural Machine Translation
+2
Suggesting Sentences for ESL using Kernel Embeddings
This method implicitly models latent intent of query and sentences, and alleviates the problem of noisy alignment.
Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations
Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams.
Japanese Predicate Conjugation for Neural Machine Translation
Neural machine translation (NMT) has a drawback in that can generate only high-frequency words owing to the computational costs of the softmax function in the output layer.
Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models
For Japanese-to-English translation, this method achieves a BLEU score that is 0. 56 points more than that of a baseline.
Complex Word Identification Based on Frequency in a Learner Corpus
We introduce the TMU systems for the Complex Word Identification (CWI) Shared Task 2018.
TMU System for SLAM-2018
We introduce the TMU systems for the second language acquisition modeling shared task 2018 (Settles et al., 2018).
Neural Machine Translation of Logographic Languages Using Sub-character Level Information
Recent neural machine translation (NMT) systems have been greatly improved by encoder-decoder models with attention mechanisms and sub-word units.
The Rule of Three: Abstractive Text Summarization in Three Bullet Points
Neural network-based approaches have become widespread for abstractive text summarization.
Neural Machine Translation of Logographic Language Using Sub-character Level Information
Recent neural machine translation (NMT) systems have been greatly improved by encoder-decoder models with attention mechanisms and sub-word units.
RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation
We introduce the RUSE metric for the WMT18 metrics shared task.
Divide and Generate: Neural Generation of Complex Sentences
We propose a task to generate a complex sentence from a simple sentence in order to amplify various kinds of responses in the database.
Chinese-Japanese Unsupervised Neural Machine Translation Using Sub-character Level Information
Unsupervised neural machine translation (UNMT) requires only monolingual data of similar language pairs during training and can produce bi-directional translation models with relatively good performance on alphabetic languages (Lample et al., 2018).
Multimodal Machine Translation with Embedding Prediction
Multimodal machine translation is an attractive application of neural machine translation (NMT).
Multi-task Learning for Japanese Predicate Argument Structure Analysis
An event-noun is a noun that has an argument structure similar to a predicate.
Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection
In this work, we investigate the effect of utilizing information not only from the final layer but also from intermediate layers of a pre-trained language representation model to detect grammatical errors.
Debiasing Word Embeddings Improves Multimodal Machine Translation
In this study, we examine various kinds of word embeddings and introduce two debiasing techniques for three multimodal NMT models and two language pairs -- English-German translation and English-French translation.
Sakura: Large-scale Incorrect Example Retrieval System for Learners of Japanese as a Second Language
This study develops an incorrect example retrieval system, called Sakura, using a large-scale Lang-8 dataset for Japanese language learners.
Controlling Grammatical Error Correction Using Word Edit Rate
In this paper, we propose a method for neural grammar error correction (GEC) that can control the degree of correction.
Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus
We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation.
Machine Translation Evaluation with BERT Regressor
We introduce the metric using BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019) for automatic machine translation evaluation.
Grammatical-Error-Aware Incorrect Example Retrieval System for Learners of Japanese as a Second Language
Existing example retrieval systems do not include grammatically incorrect examples or present only a few examples, if any.
TMU Transformer System Using BERT for Re-ranking at BEA 2019 Grammatical Error Correction on Restricted Track
Thus, it is not straightforward to utilize language representations trained from a large corpus, such as Bidirectional Encoder Representations from Transformers (BERT), in a form suitable for the learner{'}s grammatical errors.
(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus
We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation.
Filtering Pseudo-References by Paraphrasing for Automatic Evaluation of Machine Translation
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).
Improving Context-aware Neural Machine Translation with Target-side Context
We propose a weight sharing method wherein NMT saves decoder states and calculates an attention vector using the saved states when translating a current sentence.
Dynamic Fusion: Attentional Language Model for Neural Machine Translation
In prior investigations, two models have been used: a translation model and a language model.
Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019
We introduce our system that is submitted to the News Commentary task (Japanese{\textless}-{\textgreater}Russian) of the 6th Workshop on Asian Translation.
Towards Multimodal Simultaneous Neural Machine Translation
Simultaneous translation involves translating a sentence before the speaker's utterance is completed in order to realize real-time understanding in multiple languages.
Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of Japanese as a Second Language
As our corpus has less noise and its annotation scheme reflects the characteristics of the dataset, it is ideal as an evaluation corpus for correcting grammatical errors in sentences written by JSL learners.
Automated Essay Scoring System for Nonnative Japanese Learners
In this study, we created an automated essay scoring (AES) system for nonnative Japanese learners using an essay dataset with annotations for a holistic score and multiple trait scores, including content, organization, and language scores.
Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model
In this study, we explore the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC.
Ranked #12 on
Grammatical Error Correction
on CoNLL-2014 Shared Task
Keyframe Segmentation and Positional Encoding for Video-guided Machine Translation Challenge 2020
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 text.
Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition
The primary limitation of North Korean to English translation is the lack of a parallel corpus; therefore, high translation accuracy cannot be achieved.
Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner's Error Tendency
To address the limitations of language and computational resources, we assume that introducing pseudo errors into sentences similar to those written by the language learners is more efficient, rather than incorporating random pseudo errors into monolingual data.
English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs
We introduce our TMU system that is submitted to The 4th Workshop on Neural Generation and Translation (WNGT2020) to English-to-Japanese (En→Ja) track on Simultaneous Translation And Paraphrase for Language Education (STAPLE) shared task.
Double Attention-based Multimodal Neural Machine Translation with Semantic Image Regions
In contrast, we propose the application of semantic image regions for MNMT by integrating visual and textual features using two individual attention mechanisms (double attention).
Chinese Grammatical Correction Using BERT-based Pre-trained Model
In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization.
Generating Diverse Corrections with Local Beam Search for Grammatical Error Correction
In this study, we propose a beam search method to obtain diverse outputs in a local sequence transduction task where most of the tokens in the source and target sentences overlap, such as in grammatical error correction (GEC).
Cross-lingual Transfer Learning for Grammatical Error Correction
Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks.
Towards a Standardized Dataset on Indonesian Named Entity Recognition
In recent years, named entity recognition (NER) tasks in the Indonesian language have undergone extensive development.
Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model
In this study, we explored the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC.
SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction
We propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction (GEC).
Comparison of Grammatical Error Correction Using Back-Translation Models
Therefore, GEC studies have developed various methods to generate pseudo data, which comprise pairs of grammatical and artificially produced ungrammatical sentences.
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation
It is assumed that this issue is caused by insufficient number of long sentences in the training data.
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding
To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer.
Neural Combinatory Constituency Parsing
We propose two fast neural combinatory models for constituency parsing: binary and multi-branching.
Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings
In this study, we propose a model that extends the continuous space topic model (CSTM), which flexibly controls word probability in a document, using pre-trained word embeddings.
Proficiency Matters Quality Estimation in Grammatical Error Correction
This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners' proficiency with the data.
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction
Therefore, in this study, we created a quality estimation dataset with manual evaluation to build an automatic evaluation model for Japanese GEC.
Learning How to Translate North Korean through South Korean
Training a model using North Korean data is the most straightforward approach to solving this problem, but there is insufficient data to train NMT models.
Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan
We first defined three types of summarization units with different granularities to compare the performance of the discharge summary generation: whole sentences, clinical segments, and clauses.
Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?
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 patient is discharged.
WikiSQE: A Large-Scale Dataset for Sentence Quality Estimation in Wikipedia
Here, we propose WikiSQE, the first large-scale dataset for sentence quality estimation in Wikipedia.
Revisiting Meta-evaluation for Grammatical Error Correction
The results of improved correlations by aligning the granularity in the sentence-level meta-evaluation, suggest that edit-based metrics may have been underestimated in existing studies.
Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction
Large Language Models (LLMs) have been reported to outperform existing automatic evaluation metrics in some tasks, such as text summarization and machine translation.
TMUOU Submission for WMT20 Quality Estimation Shared Task
We introduce the TMUOU submission for the WMT20 Quality Estimation Shared Task 1: Sentence-Level Direct Assessment.
TMU-NLP System Using BERT-based Pre-trained Model to the NLP-TEA CGED Shared Task 2020
In this paper, we introduce our system for NLPTEA 2020 shared task of Chinese Grammatical Error Diagnosis (CGED).
Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation
Creation of an ancient Chinese dataset is considered a significant challenge because determining the most appropriate sense in a context is difficult and time-consuming owing to the different usages in ancient and modern Chinese.
Towards Automatic Generation of Messages Countering Online Hate Speech and Microaggressions
With the widespread use of social media, online hate is increasing, and microaggressions are receiving attention.
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction
This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners’ proficiency with the data.
TMU NMT System with Automatic Post-Editing by Multi-Source Levenshtein Transformer for the Restricted Translation Task of WAT 2022
One is lexical-constraint-aware neural machine translation (LeCA) (Chen et al., 2020), which is a method of adding RTVs at the end of input sentences.
Non-Autoregressive Grammatical Error Correction Toward a Writing Support System
There are several problems in applying grammatical error correction (GEC) to a writing support system.
Korean-to-Japanese Neural Machine Translation System using Hanja Information
In this paper, we describe our TMU neural machine translation (NMT) system submitted for the Patent task (Korean→Japanese) of the 7th Workshop on Asian Translation (WAT 2020, Nakazawa et al., 2020).
Translation of New Named Entities from English to Chinese
New things are being created and new words are constantly being added to languages worldwide.
TMU Japanese-English Multimodal Machine Translation System for WAT 2020
Subsequently, we pretrain a translation model on the augmented noisy data, and then fine-tune it on the clean data.
TMEKU System for the WAT2021 Multimodal Translation Task
We introduce our TMEKU system submitted to the English-Japanese Multimodal Translation Task for WAT 2021.
TMU NMT System with Japanese BART for the Patent task of WAT 2021
In this paper, we introduce our TMU Neural Machine Translation (NMT) system submitted for the Patent task (Korean Japanese and English Japanese) of 8th Workshop on Asian Translation (Nakazawa et al., 2021).
Machine Translation with Pre-specified Target-side Words Using a Semi-autoregressive Model
We introduce our TMU Japanese-to-English system, which employs a semi-autoregressive model, to tackle the WAT 2021 restricted translation task.
