193 citations · 1.5k across the 86 of their papers we have counts for
9 papers · 1 filter
CASICT Tibetan Word Segmentation System for MLWS2017
Jiawei Hu, Qun Liu
We participated in the MLWS 2017 on Tibetan word segmentation task, our system is trained in a unrestricted way, by introducing a baseline system and 76w tibetan segmented sentence…
Refining Source Representations with Relation Networks for Neural Machine Translation
Wen Zhang, Jiawei Hu, Yang Feng +1
Although neural machine translation (NMT) with the encoder-decoder framework has achieved great success in recent times, it still suffers from some drawbacks: RNNs tend to forget o…
Information-Propogation-Enhanced Neural Machine Translation by Relation Model
Wen Zhang, Jiawei Hu, Yang Feng +1
Even though sequence-to-sequence neural machine translation (NMT) model have achieved state-of-art performance in the recent fewer years, but it is widely concerned that the recurr…
Deep Neural Machine Translation with Linear Associative Unit
Mingxuan Wang, Zhengdong Lu, Jie Zhou +1
Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with their capability in modeling complex functions and capturing complex l…
Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search
Chris Hokamp, Qun Liu
We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model that…
Exploiting Cross-Sentence Context for Neural Machine Translation
Longyue Wang, Zhaopeng Tu, Andy Way +1
In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and inves…
Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
Iacer Calixto, Qun Liu, Nick Campbell
We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutio…
Multilingual Multi-modal Embeddings for Natural Language Processing
Iacer Calixto, Qun Liu, Nick Campbell
We propose a novel discriminative model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multi…
Incorporating Global Visual Features into Attention-Based Neural Machine Translation
Iacer Calixto, Qun Liu, Nick Campbell
We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. We uti…