Search Results for author: Jinsong Su

Found 54 papers, 25 papers with code

Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward

no code implementations14 Sep 2021 Zhe Hu, Zhiwei Cao, Hou Pong Chan, Jiachen Liu, Xinyan Xiao, Jinsong Su, Hua Wu

Controllable text generation is an appealing but challenging task, which allows users to specify particular attributes of the generated outputs.

Dialogue Generation

BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation

no code implementations ACL 2021 Yubin Ge, Ly Dinh, Xiaofeng Liu, Jinsong Su, Ziyao Lu, Ante Wang, Jana Diesner

In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper.

Text Generation

Exploring Dynamic Selection of Branch Expansion Orders for Code Generation

1 code implementation ACL 2021 Hui Jiang, Chulun Zhou, Fandong Meng, Biao Zhang, Jie zhou, Degen Huang, Qingqiang Wu, Jinsong Su

Due to the great potential in facilitating software development, code generation has attracted increasing attention recently.

Code Generation

Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning

no code implementations31 May 2021 Binbin Xie, Jinsong Su, Yubin Ge, Xiang Li, Jianwei Cui, Junfeng Yao, Bin Wang

However, such a decoder only exploits the preorder traversal based preceding actions, which are insufficient to ensure correct action predictions.

Code Generation

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

1 code implementation5 Mar 2021 Jinsong Su, Jialong Tang, Hui Jiang, Ziyao Lu, Yubin Ge, Linfeng Song, Deyi Xiong, Le Sun, Jiebo Luo

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.

Aspect-Based Sentiment Analysis

Dynamic Context-guided Capsule Network for Multimodal Machine Translation

1 code implementation4 Sep 2020 Huan Lin, Fandong Meng, Jinsong Su, Yongjing Yin, Zhengyuan Yang, Yubin Ge, Jie zhou, Jiebo Luo

Particularly, we represent the input image with global and regional visual features, we introduce two parallel DCCNs to model multimodal context vectors with visual features at different granularities.

Multimodal Machine Translation Representation Learning +1

Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer

1 code implementation ACL 2020 Chulun Zhou, Liang-Yu Chen, Jiachen Liu, Xinyan Xiao, Jinsong Su, Sheng Guo, Hua Wu

Unsupervised style transfer aims to change the style of an input sentence while preserving its original content without using parallel training data.

Denoising Style Transfer

Neural Simile Recognition with Cyclic Multitask Learning and Local Attention

1 code implementation19 Dec 2019 Jiali Zeng, Linfeng Song, Jinsong Su, Jun Xie, Wei Song, Jiebo Luo

Simile recognition is to detect simile sentences and to extract simile components, i. e., tenors and vehicles.

Sentence Classification

Iterative Dual Domain Adaptation for Neural Machine Translation

no code implementations IJCNLP 2019 Jiali Zeng, Yang Liu, Jinsong Su, Yubin Ge, Yaojie Lu, Yongjing Yin, Jiebo Luo

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.

Domain Adaptation Knowledge Distillation +3

Graph-based Neural Sentence Ordering

1 code implementation16 Dec 2019 Yongjing Yin, Linfeng Song, Jinsong Su, Jiali Zeng, Chulun Zhou, Jiebo Luo

Sentence ordering is to restore the original paragraph from a set of sentences.

Sentence Ordering

Grounding-Tracking-Integration

no code implementations13 Dec 2019 Zhengyuan Yang, Tushar Kumar, Tianlang Chen, Jinsong Su, Jiebo Luo

In this paper, we study Tracking by Language that localizes the target box sequence in a video based on a language query.

A Real-time Global Inference Network for One-stage Referring Expression Comprehension

1 code implementation7 Dec 2019 Yiyi Zhou, Rongrong Ji, Gen Luo, Xiaoshuai Sun, Jinsong Su, Xinghao Ding, Chia-Wen Lin, Qi Tian

Referring Expression Comprehension (REC) is an emerging research spot in computer vision, which refers to detecting the target region in an image given an text description.

Feature Selection Referring Expression Comprehension

Exploiting Temporal Relationships in Video Moment Localization with Natural Language

1 code implementation11 Aug 2019 Songyang Zhang, Jinsong Su, Jiebo Luo

We address the problem of video moment localization with natural language, i. e. localizing a video segment described by a natural language sentence.

Optical Flow Estimation

Neural Collective Entity Linking Based on Recurrent Random Walk Network Learning

1 code implementation20 Jun 2019 Mengge Xue, Weiming Cai, Jinsong Su, Linfeng Song, Yubin Ge, Yubao Liu, Bin Wang

However, most neural collective EL methods depend entirely upon neural networks to automatically model the semantic dependencies between different EL decisions, which lack of the guidance from external knowledge.

Entity Disambiguation Entity Linking +1

Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis

1 code implementation ACL 2019 Jialong Tang, Ziyao Lu, Jinsong Su, Yubin Ge, Linfeng Song, Le Sun, Jiebo Luo

In aspect-level sentiment classification (ASC), it is prevalent to equip dominant neural models with attention mechanisms, for the sake of acquiring the importance of each context word on the given aspect.

Aspect-Based Sentiment Analysis

Semantic Neural Machine Translation using AMR

1 code implementation TACL 2019 Linfeng Song, Daniel Gildea, Yue Zhang, Zhiguo Wang, Jinsong Su

It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models.

Machine Translation Translation

Towards Linear Time Neural Machine Translation with Capsule Networks

no code implementations IJCNLP 2019 Mingxuan Wang, Jun Xie, Zhixing Tan, Jinsong Su, Deyi Xiong, Lei LI

In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as \textsc{CapsNMT}.

Machine Translation Translation

Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks

3 code implementations EMNLP 2018 Biao Zhang, Deyi Xiong, Jinsong Su, Qian Lin, Huiji Zhang

Experiments on WMT14 translation tasks demonstrate that ATR-based neural machine translation can yield competitive performance on English- German and English-French language pairs in terms of both translation quality and speed.

Chinese Word Segmentation Machine Translation +2

Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT

1 code implementation24 Jul 2018 Jing Yang, Biao Zhang, Yue Qin, Xiangwen Zhang, Qian Lin, Jinsong Su

Although neural machine translation(NMT) yields promising translation performance, it unfortunately suffers from over- and under-translation is- sues [Tu et al., 2016], of which studies have become research hotspots in NMT.

Machine Translation Translation

Deconvolution-Based Global Decoding for Neural Machine Translation

1 code implementation COLING 2018 Junyang Lin, Xu sun, Xuancheng Ren, Shuming Ma, Jinsong Su, Qi Su

A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order.

Machine Translation Translation

GroupCap: Group-Based Image Captioning With Structured Relevance and Diversity Constraints

no code implementations CVPR 2018 Fuhai Chen, Rongrong Ji, Xiaoshuai Sun, Yongjian Wu, Jinsong Su

In offline optimization, we adopt an end-to-end formulation, which jointly trains the visual tree parser, the structured relevance and diversity constraints, as well as the LSTM based captioning model.

Image Captioning

Accelerating Neural Transformer via an Average Attention Network

1 code implementation ACL 2018 Biao Zhang, Deyi Xiong, Jinsong Su

To alleviate this issue, we propose an average attention network as an alternative to the self-attention network in the decoder of the neural Transformer.

Translation

Variational Recurrent Neural Machine Translation

no code implementations16 Jan 2018 Jinsong Su, Shan Wu, Deyi Xiong, Yaojie Lu, Xianpei Han, Biao Zhang

Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper.

Machine Translation Translation

Asynchronous Bidirectional Decoding for Neural Machine Translation

2 code implementations16 Jan 2018 Xiangwen Zhang, Jinsong Su, Yue Qin, Yang Liu, Rongrong Ji, Hongji Wang

The dominant neural machine translation (NMT) models apply unified attentional encoder-decoder neural networks for translation.

Machine Translation Translation

A GRU-Gated Attention Model for Neural Machine Translation

no code implementations27 Apr 2017 Biao Zhang, Deyi Xiong, Jinsong Su

In this paper, we propose a novel GRU-gated attention model (GAtt) for NMT which enhances the degree of discrimination of context vectors by enabling source representations to be sensitive to the partial translation generated by the decoder.

Machine Translation Translation

Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation

no code implementations25 Sep 2016 Jinsong Su, Zhixing Tan, Deyi Xiong, Rongrong Ji, Xiaodong Shi, Yang Liu

Neural machine translation (NMT) heavily relies on word-level modelling to learn semantic representations of input sentences.

Machine Translation Tokenization +1

Cseq2seq: Cyclic Sequence-to-Sequence Learning

no code implementations29 Jul 2016 Biao Zhang, Deyi Xiong, Jinsong Su

The vanilla sequence-to-sequence learning (seq2seq) reads and encodes a source sequence into a fixed-length vector only once, suffering from its insufficiency in modeling structural correspondence between the source and target sequence.

Machine Translation Translation +1

Variational Neural Machine Translation

1 code implementation EMNLP 2016 Biao Zhang, Deyi Xiong, Jinsong Su, Hong Duan, Min Zhang

Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence.

Machine Translation Translation

BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings

1 code implementation25 May 2016 Biao Zhang, Deyi Xiong, Jinsong Su

In this paper, we propose a bidimensional attention based recursive autoencoder (BattRAE) to integrate clues and sourcetarget interactions at multiple levels of granularity into bilingual phrase representations.

Semantic Similarity Semantic Textual Similarity

Neural Discourse Relation Recognition with Semantic Memory

no code implementations12 Mar 2016 Biao Zhang, Deyi Xiong, Jinsong Su

Inspired by this, we propose a neural recognizer for implicit discourse relation analysis, which builds upon a semantic memory that stores knowledge in a distributed fashion.

Word Embeddings

Variational Neural Discourse Relation Recognizer

1 code implementation EMNLP 2016 Biao Zhang, Deyi Xiong, Jinsong Su, Qun Liu, Rongrong Ji, Hong Duan, Min Zhang

In order to perform efficient inference and learning, we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable, and employ these approximated distributions to optimize a reparameterized variational lower bound.

Language understanding

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