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20192021
most citedDynamic Context-guided Capsule Network for Multimodal Machine Translation

57 citations · 86 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CL2021

Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings

Shaopeng Lai, Ante Wang, Fandong Meng +6

Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models,…

cs.CL2021

Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning

Jinsong Su, Jialong Tang, Hui Jiang +6

In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction. Ho…

cs.CL2021

Structural Information Preserving for Graph-to-Text Generation

Linfeng Song, Ante Wang, Jinsong Su +4

The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or…

cs.CL202057 cited

Dynamic Context-guided Capsule Network for Multimodal Machine Translation

Huan Lin, Fandong Meng, Jinsong Su +5

Multimodal machine translation (MMT), which mainly focuses on enhancing text-only translation with visual features, has attracted considerable attention from both computer vision a…

cs.CL2019

Iterative Dual Domain Adaptation for Neural Machine Translation

Jiali Zeng, Yang Liu, Jinsong Su +4

Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model.…

cs.CL201912 cited

Neural Collective Entity Linking Based on Recurrent Random Walk Network Learning

Mengge Xue, Weiming Cai, Jinsong Su +4

Benefiting from the excellent ability of neural networks on learning semantic representations, existing studies for entity linking (EL) have resorted to neural networks to exploit…