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Found 63 papers, 7 papers with code
NICT‘s Submission To WAT 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?
In this paper we describe our team‘s (NICT-5) Neural Machine Translation (NMT) models whose translations were submitted to shared tasks of the 7th Workshop on Asian Translation.
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).
NICT’s Submission to the WAT 2022 Structured Document Translation Task
However, to our surprise, we find that existing multilingual NMT systems are able to handle the translation of text annotated with XML tags without any explicit training on data containing said tags.
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).
FeatureBART: Feature Based Sequence-to-Sequence Pre-Training for Low-Resource NMT
In this paper we present FeatureBART, a linguistically motivated sequence-to-sequence monolingual pre-training strategy in which syntactic features such as lemma, part-of-speech and dependency labels are incorporated into the span prediction based pre-training framework (BART).
NICT-5’s Submission To WAT 2021: MBART Pre-training And In-Domain Fine Tuning For Indic Languages
The objective of the task was to explore the utility of multilingual approaches using a variety of in-domain and out-of-domain parallel and monolingual corpora.
Investigating Softmax Tempering for Training Neural Machine Translation Models
In low-resource scenarios and NMT models tend to perform poorly because the model training quickly converges to a point where the softmax distribution computed using logits approaches the gold label distribution.
Studying The Impact Of Document-level Context On Simultaneous Neural Machine Translation
To this end and in this paper and we propose wait-k simultaneous document-level NMT where we keep the context encoder as it is and replace the source sentence encoder and target language decoder with their wait-k equivalents.
Kyoto University MT System Description for IWSLT 2017
We describe here our Machine Translation (MT) model and the results we obtained for the IWSLT 2017 Multilingual Shared Task.
Sophisticated Lexical Databases - Simplified Usage: Mobile Applications and Browser Plugins For Wordnets
India is a country with 22 officially recognized languages and 17 of these have WordNets, a crucial resource.
Overview of the 7th Workshop on Asian Translation
This paper presents the results of the shared tasks from the 7th workshop on Asian translation (WAT2020).
Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models
This paper investigates a combination of SD and TL for training efficient NMT models for ELR settings, where we utilize TL with helping corpora twice: once for distilling the ELR corpora and then during compact model training.
IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation metrics for Indian Languages
In this paper, we fill this gap by creating an MQM dataset consisting of 7000 fine-grained annotations, spanning 5 Indian languages and 7 MT systems, and use it to establish correlations between annotator scores and scores obtained using existing automatic metrics.
MT Metrics Correlate with Human Ratings of Simultaneous Speech Translation
Our studies reveal that the offline MT metrics correlate with CR and can be reliably used for evaluating machine translation in the simultaneous mode, with some limitations on the test set size.
MorisienMT: A Dataset for Mauritian Creole Machine Translation
In this paper, we describe MorisienMT, a dataset for benchmarking machine translation quality of Mauritian Creole.
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).
IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages
Natural Language Generation (NLG) for non-English languages is hampered by the scarcity of datasets in these languages.
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages
We, then, evaluate the impact of our cognate detection mechanism on neural machine translation (NMT), as a downstream task.
Cross-Lingual Information Retrieval
Cross-Lingual Word Embeddings
+5
IndicBART: A Pre-trained Model for Indic Natural Language Generation
We present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English.
YANMTT: Yet Another Neural Machine Translation Toolkit
In this paper we present our open-source neural machine translation (NMT) toolkit called "Yet Another Neural Machine Translation Toolkit" abbreviated as YANMTT which is built on top of the Transformers library.
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation
Finally, we analyze the effects of recurrently stacked layers by visualizing the attentions of models that use recurrently stacked layers and models that do not.
Simultaneous Multi-Pivot Neural Machine Translation
Parallel corpora are indispensable for training neural machine translation (NMT) models, and parallel corpora for most language pairs do not exist or are scarce.
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.
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation
In this study, linguistic knowledge at different levels are incorporated into the neural machine translation (NMT) framework to improve translation quality for language pairs with extremely limited data.
Softmax Tempering for Training Neural Machine Translation Models
Neural machine translation (NMT) models are typically trained using a softmax cross-entropy loss where the softmax distribution is compared against smoothed gold labels.
Pre-training via Leveraging Assisting Languages for Neural Machine Translation
Sequence-to-sequence (S2S) pre-training using large monolingual data is known to improve performance for various S2S NLP tasks.
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
Monolingual pre-training approaches such as MASS (MAsked Sequence to Sequence) are extremely effective in boosting NMT quality for languages with small parallel corpora.
Balancing Cost and Benefit with Tied-Multi Transformers
We propose and evaluate a novel procedure for training multiple Transformers with tied parameters which compresses multiple models into one enabling the dynamic choice of the number of encoder and decoder layers during decoding.
Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation
To this end, we propose to exploit monolingual corpora of other languages to complement the scarcity of monolingual corpora for the LOI.
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.
Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation
To address this, we examine a language independent framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures at Coursera.
NICT's participation to WAT 2019: Multilingualism and Multi-step Fine-Tuning for Low Resource NMT
In this paper we describe our submissions to WAT 2019 for the following tasks: English{--}Tamil translation and Russian{--}Japanese translation.
Overview of the 6th Workshop on Asian Translation
This paper presents the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) including Ja↔En, Ja↔Zh scientific paper translation subtasks, Ja↔En, Ja↔Ko, Ja↔En patent translation subtasks, Hi↔En, My↔En, Km↔En, Ta↔En mixed domain subtasks and Ru↔Ja news commentary translation task.
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.
Multi-Layer Softmaxing during Training Neural Machine Translation for Flexible Decoding with Fewer Layers
This paper proposes a novel procedure for training an encoder-decoder based deep neural network which compresses NxM models into a single model enabling us to dynamically choose the number of encoder and decoder layers for decoding.
NICT's Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task
al., 2017) to improve translation quality for Japanese↔English.
NICT's Supervised Neural Machine Translation Systems for the WMT19 News Translation Task
In this paper, we describe our supervised neural machine translation (NMT) systems that we developed for the news translation task for Kazakh↔English, Gujarati↔English, Chinese↔English, and English→Finnish translation directions.
NICT's Machine Translation Systems for the WMT19 Similar Language Translation Task
Our primary submission to the task is the result of a simple combination of our SMT and NMT systems.
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation
This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmarking.
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.
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.
Recurrent Stacking of Layers for Compact Neural Machine Translation Models
In neural machine translation (NMT), the most common practice is to stack a number of recurrent or feed-forward layers in the encoder and the decoder.
Neural Machine Translation: Basics, Practical Aspects and Recent Trends
Machine Translation (MT) is a sub-field of NLP which has experienced a number of paradigm shifts since its inception.
Kyoto University Participation to WAT 2017
We describe here our approaches and results on the WAT 2017 shared translation tasks.
MMCR4NLP: Multilingual Multiway Corpora Repository for Natural Language Processing
Multilinguality is gradually becoming ubiquitous in the sense that more and more researchers have successfully shown that using additional languages help improve the results in many Natural Language Processing tasks.
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).
Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages
In this paper, we explore a simple solution to "Multi-Source Neural Machine Translation" (MSNMT) which only relies on preprocessing a N-way multilingual corpus without modifying the Neural Machine Translation (NMT) architecture or training procedure.
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).
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.
