35 citations · 142 across the 18 of their papers we have counts for
18 papers
RoBLEURT Submission for the WMT2021 Metrics Task
Yu Wan, Dayiheng Liu, Baosong Yang +6
In this paper, we present our submission to Shared Metrics Task: RoBLEURT (Robustly Optimizing the training of BLEURT). After investigating the recent advances of trainable metrics…
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation
Xiangpeng Wei, Heng Yu, Yue Hu +4
The principal task in supervised neural machine translation (NMT) is to learn to generate target sentences conditioned on the source inputs from a set of parallel sentence pairs, a…
QEMind: Alibaba's Submission to the WMT21 Quality Estimation Shared Task
Jiayi Wang, Ke Wang, Boxing Chen +3
Quality Estimation, as a crucial step of quality control for machine translation, has been explored for years. The goal is to investigate automatic methods for estimating the quali…
KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation
Xin Liu, Dayiheng Liu, Baosong Yang +6
Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently. Howeve…
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables
Weizhi Wang, Zhirui Zhang, Yichao Du +3
Zero-shot translation, directly translating between language pairs unseen in training, is a promising capability of multilingual neural machine translation (NMT). However, it usual…
Non-Parametric Online Learning from Human Feedback for Neural Machine Translation
Dongqi Wang, Haoran Wei, Zhirui Zhang +3
We study the problem of online learning with human feedback in the human-in-the-loop machine translation, in which the human translators revise the machine-generated translations a…
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation
Xin Zheng, Zhirui Zhang, Shujian Huang +4
Recently, NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level -nearest-…
Task-Oriented Dialogue System as Natural Language Generation
Weizhi Wang, Zhirui Zhang, Junliang Guo +3
In this paper, we propose to formulate the task-oriented dialogue system as the purely natural language generation task, so as to fully leverage the large-scale pre-trained models…
Towards Variable-Length Textual Adversarial Attacks
Junliang Guo, Zhirui Zhang, Linlin Zhang +4
Adversarial attacks have shown the vulnerability of machine learning models, however, it is non-trivial to conduct textual adversarial attacks on natural language processing tasks…
Exploiting Neural Query Translation into Cross Lingual Information Retrieval
Liang Yao, Baosong Yang, Haibo Zhang +2
As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel tra…
Constraint Translation Candidates: A Bridge between Neural Query Translation and Cross-lingual Information Retrieval
Tianchi Bi, Liang Yao, Baosong Yang +3
Query translation (QT) is a key component in cross-lingual information retrieval system (CLIR). With the help of deep learning, neural machine translation (NMT) has shown promising…
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation
Xiangpeng Wei, Heng Yu, Yue Hu +3
As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid cou…
Iterative Domain-Repaired Back-Translation
Hao-Ran Wei, Zhirui Zhang, Boxing Chen +1
In this paper, we focus on the domain-specific translation with low resources, where in-domain parallel corpora are scarce or nonexistent. One common and effective strategy for thi…
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
Xiangyu Duan, Baijun Ji, Hao Jia +5
In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary. Motivated by the abi…
Multiscale Collaborative Deep Models for Neural Machine Translation
Xiangpeng Wei, Heng Yu, Yue Hu +3
Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train. In this paper, we present a Multi…
AR: Auto-Repair the Synthetic Data for Neural Machine Translation
Shanbo Cheng, Shaohui Kuang, Rongxiang Weng +3
Compared with only using limited authentic parallel data as training corpus, many studies have proved that incorporating synthetic parallel data, which generated by back translatio…
GRET: Global Representation Enhanced Transformer
Rongxiang Weng, Haoran Wei, Shujian Huang +4
Transformer, based on the encoder-decoder framework, has achieved state-of-the-art performance on several natural language generation tasks. The encoder maps the words in the input…
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation
Baijun Ji, Zhirui Zhang, Xiangyu Duan +3
Transfer learning between different language pairs has shown its effectiveness for Neural Machine Translation (NMT) in low-resource scenario. However, existing transfer methods inv…