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Found 135 papers, 12 papers with code
Neural Machine Translation with Universal Visual Representation
Though visual information has been introduced for enhancing neural machine translation (NMT), its effectiveness strongly relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations.
Khmer Word Segmentation Using Conditional Random Fields
The trained CRF segmenter was compared empirically to a baseline approach based on maximum matching that used a dictionary extracted from the manually segmented corpus.
CytonMT: an Efficient Neural Machine Translation Open-source Toolkit Implemented in C++
This paper presents an open-source neural machine translation toolkit named CytonMT (https://github. com/arthurxlw/cytonMt).
Explicit Sentence Compression for Neural Machine Translation
In this paper, we propose an explicit sentence compression method to enhance the source sentence representation for NMT.
Smoothing Dialogue States for Open Conversational Machine Reading
In this work, we propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation to provide a richer dialogue state reference.
Incorporating Word Attention into Character-Based Word Segmentation
Neural network models have been actively applied to word segmentation, especially Chinese, because of the ability to minimize the effort in feature engineering.
Ranked #2 on
Japanese Word Segmentation
on BCCWJ
Exploring Recombination for Efficient Decoding of Neural Machine Translation
In Neural Machine Translation (NMT), the decoder can capture the features of the entire prediction history with neural connections and representations.
User-Generated Text Corpus for Evaluating Japanese Morphological Analysis and Lexical Normalization
Morphological analysis (MA) and lexical normalization (LN) are both important tasks for Japanese user-generated text (UGT).
Extending the Subwording Model of Multilingual Pretrained Models for New Languages
Multilingual pretrained models are effective for machine translation and cross-lingual processing because they contain multiple languages in one model.
Instance Weighting for Neural Machine Translation Domain Adaptation
Instance weighting has been widely applied to phrase-based machine translation domain adaptation.
Reference Language based Unsupervised Neural Machine Translation
Further enriching the idea of pivot translation by extending the use of parallel corpora beyond the source-target paradigm, we propose a new reference language-based framework for UNMT, RUNMT, in which the reference language only shares a parallel corpus with the source, but this corpus still indicates a signal clear enough to help the reconstruction training of UNMT through a proposed reference agreement mechanism.
Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation
Traditional Neural machine translation (NMT) involves a fixed training procedure where each sentence is sampled once during each epoch.
Syntax-Directed Attention for Neural Machine Translation
In this paper, we extend local attention with syntax-distance constraint, to focus on syntactically related source words with the predicted target word, thus learning a more effective context vector for word prediction.
Neural Machine Translation with Supervised Attention
The attention mechanisim is appealing for neural machine translation, since it is able to dynam- ically encode a source sentence by generating a alignment between a target word and source words.
A Novel Bilingual Word Embedding Method for Lexical Translation Using Bilingual Sense Clique
Most of the existing methods for bilingual word embedding only consider shallow context or simple co-occurrence information.
NICT's Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task
Using the clean data of the WMT18 shared news translation task, we designed several features and trained a classifier to score each sentence pairs in the noisy data.
NICT's Neural and Statistical Machine Translation Systems for the WMT18 News Translation Task
Our systems are ranked first for the Estonian-English and Finnish-English language pairs (constraint) according to BLEU-cased.
Forest-Based Neural Machine Translation
Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors.
Simplified Abugidas
An abugida is a writing system where the consonant letters represent syllables with a default vowel and other vowels are denoted by diacritics.
Sentence Embedding for Neural Machine Translation Domain Adaptation
Although new corpora are becoming increasingly available for machine translation, only those that belong to the same or similar domains are typically able to improve translation performance.
Neural Machine Translation with Source Dependency Representation
Source dependency information has been successfully introduced into statistical machine translation.
Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation
A large-scale parallel corpus is required to train encoder-decoder neural machine translation.
NICT Self-Training Approach to Neural Machine Translation at NMT-2018
This paper describes the NICT neural machine translation system submitted at the NMT-2018 shared task.
Japanese to English/Chinese/Korean Datasets for Translation Quality Estimation and Automatic Post-Editing
Aiming at facilitating the research on quality estimation (QE) and automatic post-editing (APE) of machine translation (MT) outputs, especially for those among Asian languages, we have created new datasets for Japanese to English, Chinese, and Korean translations.
Ensemble and Reranking: Using Multiple Models in the NICT-2 Neural Machine Translation System at WAT2017
In this paper, we describe the NICT-2 neural machine translation system evaluated at WAT2017.
A Simple and Strong Baseline: NAIST-NICT Neural Machine Translation System for WAT2017 English-Japanese Translation Task
This paper describes the details about the NAIST-NICT machine translation system for WAT2017 English-Japanese Scientific Paper Translation Task.
Global Pre-ordering for Improving Sublanguage Translation
When translating formal documents, capturing the sentence structure specific to the sublanguage is extremely necessary to obtain high-quality translations.
NICT-2 Translation System for WAT2016: Applying Domain Adaptation to Phrase-based Statistical Machine Translation
This paper describes the NICT-2 translation system for the 3rd Workshop on Asian Translation.
An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation
Simultaneous interpretation is a very challenging application of machine translation in which the input is a stream of words from a speech recognition engine.
Similar Southeast Asian Languages: Corpus-Based Case Study on Thai-Laotian and Malay-Indonesian
This paper illustrates the similarity between Thai and Laotian, and between Malay and Indonesian, based on an investigation on raw parallel data from Asian Language Treebank.
A Prototype Automatic Simultaneous Interpretation System
Simultaneous interpretation allows people to communicate spontaneously across language boundaries, but such services are prohibitively expensive for the general public.
MuTUAL: A Controlled Authoring Support System Enabling Contextual Machine Translation
The paper introduces a web-based authoring support system, MuTUAL, which aims to help writers create multilingual texts.
Context-Aware Smoothing for Neural Machine Translation
In Neural Machine Translation (NMT), each word is represented as a low-dimension, real-value vector for encoding its syntax and semantic information.
Key-value Attention Mechanism for Neural Machine Translation
In this paper, we propose a neural machine translation (NMT) with a key-value attention mechanism on the source-side encoder.
Improving Neural Machine Translation with Neural Syntactic Distance
The explicit use of syntactic information has been proved useful for neural machine translation (NMT).
Introducing the Asian Language Treebank (ALT)
The project has so far created a corpus for Myanmar and will extend in scope to include other languages in the near future.
ASPEC: Asian Scientific Paper Excerpt Corpus
In this paper, we describe the details of the ASPEC (Asian Scientific Paper Excerpt Corpus), which is the first large-size parallel corpus of scientific paper domain.
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation
In previous methods, UBWE is first trained using non-parallel monolingual corpora and then this pre-trained UBWE is used to initialize the word embedding in the encoder and decoder of UNMT.
Neural Machine Translation with Reordering Embeddings
The reordering model plays an important role in phrase-based statistical machine translation.
Sentence-Level Agreement for Neural Machine Translation
The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references.
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 Unsupervised Neural and Statistical Machine Translation Systems for the WMT19 News Translation Task
This paper presents the NICT{'}s participation in the WMT19 unsupervised news translation task.
NICT's Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task
al., 2017) to improve translation quality for Japanese↔English.
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.
Hybrid Data-Model Parallel Training for Sequence-to-Sequence Recurrent Neural Network Machine Translation
We apply a model parallel approach to the RNN encoder-decoder part of the Seq2Seq model and a data parallel approach to the attention-softmax part of the model.
Document-level Neural Machine Translation with Associated Memory Network
Most existing document-level NMT approaches are satisfied with a smattering sense of global document-level information, while this work focuses on exploiting detailed document-level context in terms of a memory network.
Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English
This paper presents the NICT{'}s supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks.
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.
English-Myanmar Supervised and Unsupervised NMT: NICT's Machine Translation Systems at WAT-2019
This paper presents the NICT{'}s participation (team ID: NICT) in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (Burmese) - English task in both translation directions.
Long Warm-up and Self-Training: Training Strategies of NICT-2 NMT System at WAT-2019
This paper describes the NICT-2 neural machine translation system at the 6th Workshop on Asian Translation.
Recycling a Pre-trained BERT Encoder for Neural Machine Translation
In this paper, a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model is applied to Transformer-based neural machine translation (NMT).
SJTU-NICT at MRP 2019: Multi-Task Learning for End-to-End Uniform Semantic Graph Parsing
This paper describes our SJTU-NICT{'}s system for participating in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL).
Recurrent Positional Embedding for Neural Machine Translation
To address this issue, this work proposes a recurrent positional embedding approach based on word vector.
MY-AKKHARA: A Romanization-based Burmese (Myanmar) Input Method
MY-AKKHARA is a method used to input Burmese texts encoded in the Unicode standard, based on commonly accepted Latin transcription.
Probing Contextualized Sentence Representations with Visual Awareness
We present a universal framework to model contextualized sentence representations with visual awareness that is motivated to overcome the shortcomings of the multimodal parallel data with manual annotations.
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.
Modeling Future Cost for Neural Machine Translation
Existing neural machine translation (NMT) systems utilize sequence-to-sequence neural networks to generate target translation word by word, and then make the generated word at each time-step and the counterpart in the references as consistent as possible.
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training
Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community.
Explicit Reordering for Neural Machine Translation
Thus, we propose a novel reordering method to explicitly model this reordering information for the Transformer-based NMT.
Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios
Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks.
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs.
A Myanmar (Burmese)-English Named Entity Transliteration Dictionary
For the Myanmar (Burmese) language, robust automatic transliteration for borrowed English words is a challenging task because of the complex Myanmar writing system and the lack of data.
Content Word Aware Neural Machine Translation
Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word.
A Three-Parameter Rank-Frequency Relation in Natural Languages
We present that, the rank-frequency relation in textual data follows $f \propto r^{-\alpha}(r+\gamma)^{-\beta}$, where $f$ is the token frequency and $r$ is the rank by frequency, with ($\alpha$, $\beta$, $\gamma$) as parameters.
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.
A System for Worldwide COVID-19 Information Aggregation
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.
Data-dependent Gaussian Prior Objective for Language Generation
However, MLE focuses on once-to-all matching between the predicted sequence and gold-standard, consequently treating all incorrect predictions as being equally incorrect.
Prior Knowledge Representation for Self-Attention Networks
Self-attention networks (SANs) have shown promising empirical results in various natural language processing tasks.
Cross-lingual Transfer Learning for Pre-trained Contextualized Language Models
Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant.
SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
In this paper, we introduced our joint team SJTU-NICT 's participation in the WMT 2020 machine translation shared task.
Text Compression-aided Transformer Encoding
In this paper, we propose explicit and implicit text compression approaches to enhance the Transformer encoding and evaluate models using this approach on several typical downstream tasks that rely on the encoding heavily.
Cross-lingual Transferring of Pre-trained Contextualized Language Models
Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant.
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.
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.
Bilingual Subword Segmentation for Neural Machine Translation
This paper proposed a new subword segmentation method for neural machine translation, {``}Bilingual Subword Segmentation,{''} which tokenizes sentences to minimize the difference between the number of subword units in a sentence and that of its translation.
Intermediate Self-supervised Learning for Machine Translation Quality Estimation
The proposed method does not rely on annotated data and is complementary to QE methods involving pre-trained sentence encoders and domain adaptation.
NICT’s Neural Machine Translation Systems for the WAT21 Restricted Translation Task
This paper describes our system (Team ID: nictrb) for participating in the WAT’21 restricted machine translation task.
NICT-2 Translation System at WAT-2021: Applying a Pretrained Multilingual Encoder-Decoder Model to Low-resource Language Pairs
In this paper, we present the NICT system (NICT-2) submitted to the NICT-SAP shared task at the 8th Workshop on Asian Translation (WAT-2021).
Multi-domain Adaptation for Statistical Machine Translation Based on Feature Augmentation
In this paper, we present domain adaptation methods for machine translation that assume multiple domains.
Unsupervised Neural Machine Translation with Universal Grammar
Machine translation usually relies on parallel corpora to provide parallel signals for training.
MiSS: An Assistant for Multi-Style Simultaneous Translation
With this system, we aim to provide a complete translation experience for machine translation users.
Transformer-based Double-token Bidirectional Autoregressive Decoding in Neural Machine Translation
This paper presents a simple method that extends a standard Transformer-based autoregressive decoder, to speed up decoding.
A Text Editing Approach to Joint Japanese Word Segmentation, POS Tagging, and Lexical Normalization
Lexical normalization, in addition to word segmentation and part-of-speech tagging, is a fundamental task for Japanese user-generated text processing.
SJTU-NICT’s Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
In this paper, we introduced our joint team SJTU-NICT ‘s participation in the WMT 2020 machine translation shared task.
MiSS@WMT21: Contrastive Learning-reinforced Domain Adaptation in Neural Machine Translation
In this paper, we describe our MiSS system that participated in the WMT21 news translation task.
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation
Inspired by this discovery, we then propose approaches to improving it, with respect to model structure and model training, to make the deep decoder practical in NMT.
Synchronous Refinement for Neural Machine Translation
Machine translation typically adopts an encoder-to-decoder framework, in which the decoder generates the target sentence word-by-word in an auto-regressive manner.
Restricted or Not: A General Training Framework for Neural Machine Translation
Although this can satisfy the requirements overall, it usually requires a larger beam size and far longer decoding time than unrestricted translation, which limits the concurrent processing ability of the translation model in deployment, and thus its practicality.
A Multimodal Simultaneous Interpretation Prototype: Who Said What
“Who said what” is essential for users to understand video streams that have more than one speaker, but conventional simultaneous interpretation systems merely present “what was said” in the form of subtitles.
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).
Multi-Source Cross-Lingual Constituency Parsing
Pretrained multilingual language models have become a key part of cross-lingual transfer for many natural language processing tasks, even those without bilingual information.
Language Model Pre-training on True Negatives
Discriminative pre-trained language models (PLMs) learn to predict original texts from intentionally corrupted ones.
Universal Multimodal Representation for Language Understanding
Representation learning is the foundation of natural language processing (NLP).
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).
