35 citations · 142 across the 18 of their papers we have counts for
8 papers · 1 filter
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…