82 citations · 432 across the 55 of their papers we have counts for
12 papers · 1 filter
Efficient Object-Level Visual Context Modeling for Multimodal Machine Translation: Masking Irrelevant Objects Helps Grounding
Dexin Wang, Deyi Xiong
Visual context provides grounding information for multimodal machine translation (MMT). However, previous MMT models and probing studies on visual features suggest that visual info…
Modeling Homophone Noise for Robust Neural Machine Translation
Wenjie Qin, Xiang Li, Yuhui Sun +3
In this paper, we propose a robust neural machine translation (NMT) framework. The framework consists of a homophone noise detector and a syllable-aware NMT model to homophone erro…
RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling
Jun Quan, Shian Zhang, Qian Cao +2
In order to alleviate the shortage of multi-domain data and to capture discourse phenomena for task-oriented dialogue modeling, we propose RiSAWOZ, a large-scale multi-domain Chine…
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer
Yufang Huang, Wentao Zhu, Deyi Xiong +3
Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation. In this paper, we propose a novel neural approach…
Learning Hard Retrieval Decoder Attention for Transformers
Hongfei Xu, Qiuhui Liu, Josef van Genabith +1
The Transformer translation model is based on the multi-head attention mechanism, which can be parallelized easily. The multi-head attention network performs the scaled dot-product…
Rewiring the Transformer with Depth-Wise LSTMs
Hongfei Xu, Yang Song, Qiuhui Liu +2
Stacking non-linear layers allows deep neural networks to model complicated functions, and including residual connections in Transformer layers is beneficial for convergence and pe…
Learning Source Phrase Representations for Neural Machine Translation
Hongfei Xu, Josef van Genabith, Deyi Xiong +2
The Transformer translation model (Vaswani et al., 2017) based on a multi-head attention mechanism can be computed effectively in parallel and has significantly pushed forward the…
Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change
Hongfei Xu, Josef van Genabith, Deyi Xiong +1
The choice of hyper-parameters affects the performance of neural models. While much previous research (Sutskever et al., 2013; Duchi et al., 2011; Kingma and Ba, 2015) focuses on a…
Modeling Long Context for Task-Oriented Dialogue State Generation
Jun Quan, Deyi Xiong
Based on the recently proposed transferable dialogue state generator (TRADE) that predicts dialogue states from utterance-concatenated dialogue context, we propose a multi-task lea…
Shallow Discourse Annotation for Chinese TED Talks
Wanqiu Long, Xinyi Cai, James E. M. Reid +2
Text corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chines…
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation
Pei Zhang, Xu Zhang, Wei Chen +3
Document-level machine translation incorporates inter-sentential dependencies into the translation of a source sentence. In this paper, we propose a new framework to model cross-se…
Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers
Hongfei Xu, Josef van Genabith, Qiuhui Liu +1
Due to its effectiveness and performance, the Transformer translation model has attracted wide attention, most recently in terms of probing-based approaches. Previous work focuses…