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Found 76 papers, 17 papers with code
Meta Ensemble for Japanese-Chinese Neural Machine Translation: Kyoto-U+ECNU Participation to WAT 2020
This paper describes the Japanese-Chinese Neural Machine Translation (NMT) system submitted by the joint team of Kyoto University and East China Normal University (Kyoto-U+ECNU) to WAT 2020 (Nakazawa et al., 2020).
Overview of the 9th Workshop on Asian Translation
This paper presents the results of the shared tasks from the 9th workshop on Asian translation (WAT2022).
TMEKU System for the WAT2021 Multimodal Translation Task
We introduce our TMEKU system submitted to the English-Japanese Multimodal Translation Task for WAT 2021.
Overview of the 8th Workshop on Asian Translation
This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021).
Filtering of Noisy Web-Crawled Parallel Corpus: the Japanese-Bulgarian Language Pair
The corpus utilised to train machine translation models in the study is CCMatrix, provided by OPUS.
IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
We introduce the IDSOU submission for the WNUT-2020 task 2: identification of informative COVID-19 English Tweets.
Flexible Visual Grounding
Existing visual grounding datasets are artificially made, where every query regarding an entity must be able to be grounded to a corresponding image region, i. e., answerable.
Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents
This approach aims to generate relation representations that are more aware of the spatial context and unseen relation in a manner similar to human perception.
Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese
The creation of instruction data and evaluation benchmarks for serving Large language models often involves enormous human annotation.
MOS-FAD: Improving Fake Audio Detection Via Automatic Mean Opinion Score Prediction
We propose MOS-FAD, where MOS can be leveraged at two key points in FAD: training data selection and model fusion.
MM-LLMs: Recent Advances in MultiModal Large Language Models
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies.
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection
The early-stage Alzheimer's disease (AD) detection has been considered an important field of medical studies.
Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine Translation of Lecture Transcripts
To create the parallel corpora, we propose a dynamic programming based sentence alignment algorithm which leverages the cosine similarity of machine-translated sentences.
Video-Helpful Multimodal Machine Translation
In addition to the extensive training set, EVA contains a video-helpful evaluation set in which subtitles are ambiguous, and videos are guaranteed helpful for disambiguation.
DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine Reading
The use of visually-rich documents (VRDs) in various fields has created a demand for Document AI models that can read and comprehend documents like humans, which requires the overcoming of technical, linguistic, and cognitive barriers.
Vision-Enhanced Semantic Entity Recognition in Document Images via Visually-Asymmetric Consistency Learning
Extracting meaningful entities belonging to predefined categories from Visually-rich Form-like Documents (VFDs) is a challenging task.
SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation
Sub-word segmentation is an essential pre-processing step for Neural Machine Translation (NMT).
Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation
We enhance ChatGPT's ability to reason for the system's perspective by integrating in-context learning with commonsense knowledge.
Variable-length Neural Interlingua Representations for Zero-shot Neural Machine Translation
The language-independency of encoded representations within multilingual neural machine translation (MNMT) models is crucial for their generalization ability on zero-shot translation.
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation
This paper studies the impact of layer normalization (LayerNorm) on zero-shot translation (ZST).
Towards Speech Dialogue Translation Mediating Speakers of Different Languages
We present a new task, speech dialogue translation mediating speakers of different languages.
Learning More May Not Be Better: Knowledge Transferability in Vision and Language Tasks
Is more data always better to train vision-and-language models?
EMS: Efficient and Effective Massively Multilingual Sentence Representation Learning
Massively multilingual sentence representation models, e. g., LASER, SBERT-distill, and LaBSE, help significantly improve cross-lingual downstream tasks.
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?
Meanwhile, the contrastive objective can implicitly utilize automatically learned word alignment, which has not been explored in many-to-many NMT.
Fusion of Self-supervised Learned Models for MOS Prediction
This challenge aims to predict MOS scores of synthetic speech on two tracks, the main track and a more challenging sub-track: out-of-domain (OOD).
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
Low-resource speech recognition has been long-suffering from insufficient training data.
VISA: An Ambiguous Subtitles Dataset for Visual Scene-Aware Machine Translation
Existing multimodal machine translation (MMT) datasets consist of images and video captions or general subtitles, which rarely contain linguistic ambiguity, making visual information not so effective to generate appropriate translations.
Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine Translation
In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for language pairs involving Japanese as the source or target language, and English-specific sequence to sequence (ENSS) for language pairs involving English.
Transferring Domain-Agnostic Knowledge in Video Question Answering
Video question answering (VideoQA) is designed to answer a given question based on a relevant video clip.
Attending Self-Attention: A Case Study of Visually Grounded Supervision in Vision-and-Language Transformers
The impressive performances of pre-trained visually grounded language models have motivated a growing body of research investigating what has been learned during the pre-training.
Video-guided Machine Translation with Spatial Hierarchical Attention Network
Video-guided machine translation, as one type of multimodal machine translations, aims to engage video contents as auxiliary information to address the word sense ambiguity problem in machine translation.
A Picture May Be Worth a Hundred Words for Visual Question Answering
This paper delves into the effectiveness of textual representations for image understanding in the specific context of VQA.
WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations
We annotate 17, 000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset.
Lightweight Cross-Lingual Sentence Representation Learning
Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks.
GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph
In this paper, we propose a novel use of a knowledge graph, that is constructed on annotated data and pseudo-labeled data.
Understanding the Role of Scene Graphs in Visual Question Answering
Visual Question Answering (VQA) is of tremendous interest to the research community with important applications such as aiding visually impaired users and image-based search.
Multilingual Neural Machine Translation
The advent of neural machine translation (NMT) has opened up exciting research in building multilingual translation systems i. e. translation models that can handle more than one language pair.
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).
A Corpus for English-Japanese Multimodal Neural Machine Translation with Comparable Sentences
Multimodal neural machine translation (NMT) has become an increasingly important area of research over the years because additional modalities, such as image data, can provide more context to textual data.
Lexically Cohesive Neural Machine Translation with Copy Mechanism
Lexically cohesive translations preserve consistency in word choices in document-level translation.
Constructing a Visual Relationship Authenticity Dataset
Visual relationship detection is crucial for scene understanding in images.
A Dataset and Baselines for Visual Question Answering on Art
Our dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions.
Text Classification with Negative Supervision
Advanced pre-trained models for text representation have achieved state-of-the-art performance on various text classification tasks.
Annotation of Adverse Drug Reactions in Patients' Weblogs
The dataset we present in this paper is unique for the richness of annotated information, including detailed descriptions of drug reactions with full context.
Constructing a Public Meeting Corpus
In this paper, we propose a full pipeline of analysis of a large corpus about a century of public meeting in historical Australian news papers, from construction to visual exploration.
Knowledge-Based Visual Question Answering in Videos
We propose a novel video understanding task by fusing knowledge-based and video question answering.
A Comprehensive Survey of Multilingual Neural Machine Translation
We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years.
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation
This paper highlights the impressive utility of multi-parallel corpora for transfer learning in a one-to-many low-resource neural machine translation (NMT) setting.
KnowIT VQA: Answering Knowledge-Based Questions about Videos
We propose a novel video understanding task by fusing knowledge-based and video question answering.
Revisiting Simple Domain Adaptation Methods in Unsupervised Neural Machine Translation
However, it has not been well-studied for unsupervised neural machine translation (UNMT), although UNMT has recently achieved remarkable results in several domain-specific language pairs.
Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation
In this paper, we propose two novel methods for domain adaptation for the attention-only neural machine translation (NMT) model, i. e., the Transformer.
Using Natural Language Processing to Develop an Automated Orthodontic Diagnostic System
We work on the task of automatically designing a treatment plan from the findings included in the medical certificate written by the dentist.
A Brief Survey of Multilingual Neural Machine Translation
We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years.
iParaphrasing: Extracting Visually Grounded Paraphrases via an Image
These extracted VGPs have the potential to improve language and image multimodal tasks such as visual question answering and image captioning.
A Survey of Domain Adaptation for Neural Machine Translation
Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available.
Recursive Neural Network Based Preordering for English-to-Japanese Machine Translation
Experiments show that the proposed method achieves comparable gain in translation quality to the state-of-the-art method but without a manual feature design.
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation
In this paper, we propose a novel domain adaptation method named {``}mixed fine tuning{''} for neural machine translation (NMT).
An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation
In this paper, we propose a novel domain adaptation method named "mixed fine tuning" for neural machine translation (NMT).
Consistent Word Segmentation, Part-of-Speech Tagging and Dependency Labelling Annotation for Chinese Language
In this paper, we propose a new annotation approach to Chinese word segmentation, part-of-speech (POS) tagging and dependency labelling that aims to overcome the two major issues in traditional morphology-based annotation: Inconsistency and data sparsity.
Kyoto University Participation to WAT 2016
We report very good translation results, especially when using neural MT for Chinese-to-Japanese translation.
SCTB: A Chinese Treebank in Scientific Domain
Treebanks are curial for natural language processing (NLP).
Supervised Syntax-based Alignment between English Sentences and Abstract Meaning Representation Graphs
As alignment links are not given between English sentences and Abstract Meaning Representation (AMR) graphs in the AMR annotation, automatic alignment becomes indispensable for training an AMR parser.
Parallel Sentence Extraction from Comparable Corpora with Neural Network Features
Parallel corpora are crucial for machine translation (MT), however they are quite scarce for most language pairs and domains.
Paraphrasing Out-of-Vocabulary Words with Word Embeddings and Semantic Lexicons for Low Resource Statistical Machine Translation
Out-of-vocabulary (OOV) word is a crucial problem in statistical machine translation (SMT) with low resources.
Simultaneous Sentence Boundary Detection and Alignment with Pivot-based Machine Translation Generated Lexicons
Sentence alignment is a task that consists in aligning the parallel sentences in a translated article pair.
Constructing a Chinese---Japanese Parallel Corpus from Wikipedia
Using the system, we construct a Chinese―Japanese parallel corpus with more than 126k highly accurate parallel sentences from Wikipedia.
Chinese Characters Mapping Table of Japanese, Traditional Chinese and Simplified Chinese
Chinese characters are used both in Japanese and Chinese, which are called Kanji and Hanzi respectively.
