no code implementations • EMNLP (ACL) 2021 • Hai Zhao, Rui Wang, Kehai Chen
This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks that have always been beneficial from informative syntactic clues since a long time ago, though the advance from end-to-end deep learning models shows new results.
no code implementations • Findings (ACL) 2022 • Kehai Chen, Masao Utiyama, Eiichiro Sumita, Rui Wang, Min Zhang
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.
no code implementations • WMT (EMNLP) 2020 • Zuchao Li, Hai Zhao, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita
In this paper, we introduced our joint team SJTU-NICT ‘s participation in the WMT 2020 machine translation shared task.
no code implementations • 12 Feb 2024 • Zhengsheng Guo, Zhiwei He, Wenxiang Jiao, Xing Wang, Rui Wang, Kehai Chen, Zhaopeng Tu, Yong Xu, Min Zhang
Motivated by the success of unsupervised neural machine translation (UNMT), we introduce an unsupervised sign language translation and generation network (USLNet), which learns from abundant single-modality (text and video) data without parallel sign language data.
no code implementations • 30 Jan 2024 • Zhi Jing, Yongye Su, Yikun Han, Bo Yuan, Haiyun Xu, Chunjiang Liu, Kehai Chen, Min Zhang
This survey explores the synergistic potential of Large Language Models (LLMs) and Vector Databases (VecDBs), a burgeoning but rapidly evolving research area.
1 code implementation • 13 Nov 2023 • Meizhi Zhong, Lemao Liu, Kehai Chen, Mingming Yang, Min Zhang
Simultaneous Machine Translation (SiMT) aims to yield a real-time partial translation with a monotonically growing the source-side context.
no code implementations • 9 Jan 2023 • Zhuosheng Zhang, Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Zuchao Li, Hai Zhao
Representation learning is the foundation of natural language processing (NLP).
1 code implementation • NAACL 2022 • Wang Xu, Kehai Chen, Lili Mou, Tiejun Zhao
Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences.
Ranked #5 on Dialog Relation Extraction on DialogRE (F1c (v1) metric)
Dialog Relation Extraction Document-level Relation Extraction +2
2 code implementations • Findings (ACL) 2021 • Wang Xu, Kehai Chen, Tiejun Zhao
Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i. e., pattern recognition, logical reasoning, coreference reasoning, etc.)
Ranked #24 on Relation Extraction on DocRED
no code implementations • 11 Feb 2021 • Zuchao Li, Zhuosheng Zhang, Hai Zhao, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita
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.
no code implementations • 1 Jan 2021 • Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita
Self-attention networks (SANs) have shown promising empirical results in various natural language processing tasks.
1 code implementation • 21 Dec 2020 • Wang Xu, Kehai Chen, Tiejun Zhao
In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years.
Ranked #35 on Relation Extraction on DocRED
no code implementations • COLING 2020 • Zhenyu Zhao, Shuangzhi Wu, Muyun Yang, Kehai Chen, Tiejun Zhao
Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels.
no code implementations • 11 Oct 2020 • Zuchao Li, Hai Zhao, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita
In this paper, we introduced our joint team SJTU-NICT 's participation in the WMT 2020 machine translation shared task.
no code implementations • ACL 2020 • Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita
Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word.
1 code implementation • ICLR 2020 • Zhuosheng Zhang, Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Zuchao Li, Hai Zhao
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.
no code implementations • ICLR 2020 • Zuchao Li, Rui Wang, Kehai Chen, Masso Utiyama, Eiichiro Sumita, Zhuosheng Zhang, Hai Zhao
However, MLE focuses on once-to-all matching between the predicted sequence and gold-standard, consequently treating all incorrect predictions as being equally incorrect.
no code implementations • ACL 2020 • Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs.
no code implementations • NAACL 2021 • Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks.
no code implementations • 8 Apr 2020 • Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita
Thus, we propose a novel reordering method to explicitly model this reordering information for the Transformer-based NMT.
no code implementations • COLING 2020 • Haipeng Sun, Rui Wang, Kehai Chen, Xugang Lu, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community.
no code implementations • 28 Feb 2020 • Chaoqun Duan, Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Conghui Zhu, Tiejun Zhao
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.
1 code implementation • 27 Dec 2019 • Zuchao Li, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Zhuosheng Zhang, Hai Zhao
In this paper, we propose an explicit sentence compression method to enhance the source sentence representation for NMT.
no code implementations • 7 Nov 2019 • Zhuosheng Zhang, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Hai Zhao
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.
no code implementations • WS 2019 • Rui Wang, Haipeng Sun, Kehai Chen, Chenchen Ding, Masao Utiyama, Eiichiro Sumita
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.
no code implementations • IJCNLP 2019 • Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita
To address this issue, this work proposes a recurrent positional embedding approach based on word vector.
no code implementations • 31 Oct 2019 • Shu Jiang, Rui Wang, Zuchao Li, Masao Utiyama, Kehai Chen, Eiichiro Sumita, Hai Zhao, Bao-liang Lu
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.
no code implementations • 26 Aug 2019 • Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao, Chenhui Chu
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.
no code implementations • WS 2019 • Benjamin Marie, Haipeng Sun, Rui Wang, Kehai Chen, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita
This paper presents the NICT{'}s participation in the WMT19 unsupervised news translation task.
no code implementations • WS 2019 • Raj Dabre, Kehai Chen, Benjamin Marie, Rui Wang, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita
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.
no code implementations • ACL 2019 • Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita
The reordering model plays an important role in phrase-based statistical machine translation.
no code implementations • ACL 2019 • Mingming Yang, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Min Zhang, Tiejun Zhao
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.
no code implementations • ACL 2019 • Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
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.
no code implementations • ACL 2019 • Fengshun Xiao, Jiangtong Li, Hai Zhao, Rui Wang, Kehai Chen
To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training.
no code implementations • 12 Nov 2017 • Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
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.
no code implementations • IJCNLP 2017 • Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
In Neural Machine Translation (NMT), each word is represented as a low-dimension, real-value vector for encoding its syntax and semantic information.
no code implementations • EMNLP 2017 • Kehai Chen, Rui Wang, Masao Utiyama, Lemao Liu, Akihiro Tamura, Eiichiro Sumita, Tiejun Zhao
Source dependency information has been successfully introduced into statistical machine translation.
1 code implementation • EMNLP 2017 • Rui Wang, Masao Utiyama, Lemao Liu, Kehai Chen, Eiichiro Sumita
Instance weighting has been widely applied to phrase-based machine translation domain adaptation.