Search Results for author: Zhongzhou Zhao

Found 17 papers, 2 papers with code

VK-G2T: Vision and Context Knowledge enhanced Gloss2Text

no code implementations15 Dec 2023 Liqiang Jing, Xuemeng Song, Xinxing Zu, Na Zheng, Zhongzhou Zhao, Liqiang Nie

Existing sign language translation methods follow a two-stage pipeline: first converting the sign language video to a gloss sequence (i. e. Sign2Gloss) and then translating the generated gloss sequence into a spoken language sentence (i. e. Gloss2Text).

Sentence Sign Language Translation +1

CASEIN: Cascading Explicit and Implicit Control for Fine-grained Emotion Intensity Regulation

no code implementations27 Jun 2023 Yuhao Cui, Xiongwei Wang, Zhongzhou Zhao, Wei Zhou, Haiqing Chen

However, these high-level semantic probabilities are often inaccurate and unsmooth at the phoneme level, leading to bias in learning.


Stylized Data-to-Text Generation: A Case Study in the E-Commerce Domain

no code implementations5 May 2023 Liqiang Jing, Xuemeng Song, Xuming Lin, Zhongzhou Zhao, Wei Zhou, Liqiang Nie

This task is non-trivial, due to three challenges: the logic of the generated text, unstructured style reference, and biased training samples.

Attribute Data-to-Text Generation

COOP: Decoupling and Coupling of Whole-Body Grasping Pose Generation

1 code implementation ICCV 2023 Yanzhao Zheng, Yunzhou Shi, Yuhao Cui, Zhongzhou Zhao, Zhiling Luo, Wei Zhou

To address this issue, we propose a novel framework called COOP (DeCOupling and COupling of Whole-Body GrasPing Pose Generation) to synthesize life-like whole-body poses that cover the widest range of human grasping capabilities.

Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning

no code implementations6 Oct 2022 Xiong Junwu, Xiaoyun Feng, Yunzhou Shi, James Zhang, Zhongzhou Zhao, Wei Zhou

Our proposed framework learns through real-time interactions between the digital human and customers dynamically through the state-of-art RL algorithms, combined with multimodal embedding and graph embedding, to improve the accuracy of personalization and thus enable the digital human agent to timely catch the attention of the customer.

Decision Making Graph Embedding +2

Dual Preference Distribution Learning for Item Recommendation

no code implementations24 Jan 2022 Xue Dong, Xuemeng Song, Na Zheng, Yinwei Wei, Zhongzhou Zhao

Moreover, we can summarize a preferred attribute profile for each user, depicting his/her preferred item attributes.

Attribute Recommendation Systems

GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation

no code implementations18 Aug 2021 Xuming Lin, Shaobo Cui, Zhongzhou Zhao, Wei Zhou, Ji Zhang, Haiqing Chen

With these two synergic representations, we then regroup these phrases into a fine-grained plan, based on which we generate the final long text.

Story Generation

SPMoE: Generate Multiple Pattern-Aware Outputs with Sparse Pattern Mixture of Experts

no code implementations17 Aug 2021 Shaobo Cui, Xintong Bao, Xuming Lin, Zhongzhou Zhao, Ji Zhang, Wei Zhou, Haiqing Chen

Each one-to-one mapping is associated with a conditional generation pattern and is modeled with an expert in SPMoE.

Paraphrase Generation

ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration

1 code implementation16 Aug 2021 Yuhao Cui, Zhou Yu, Chunqi Wang, Zhongzhou Zhao, Ji Zhang, Meng Wang, Jun Yu

Nevertheless, most existing VLP approaches have not fully utilized the intrinsic knowledge within the image-text pairs, which limits the effectiveness of the learned alignments and further restricts the performance of their models.

Visual Reasoning

OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach

no code implementations24 Feb 2021 Shaobo Cui, Xintong Bao, Xinxing Zu, Yangyang Guo, Zhongzhou Zhao, Ji Zhang, Haiqing Chen

This pipeline approach, however, is undesired in mining the most appropriate QA pairs from documents since it ignores the connection between question generation and answer extraction, which may lead to incompatible QA pair generation, i. e., the selected answer span is inappropriate for question generation.

Machine Reading Comprehension Question Answering +2

Predict-then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems

no code implementations27 May 2020 Zehao Lin, Shaobo Cui, Guodun Li, Xiaoming Kang, Feng Ji, FengLin Li, Zhongzhou Zhao, Haiqing Chen, Yin Zhang

More specifically, we take advantage of a decision model to help the dialogue system decide whether to wait or answer.


A Deep Cascade Model for Multi-Document Reading Comprehension

no code implementations28 Nov 2018 Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen

To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the document-level and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension.

Machine Reading Comprehension Question Answering +2

Memory-augmented Dialogue Management for Task-oriented Dialogue Systems

no code implementations1 May 2018 Zheng Zhang, Minlie Huang, Zhongzhou Zhao, Feng Ji, Haiqing Chen, Xiaoyan Zhu

Dialogue management (DM) decides the next action of a dialogue system according to the current dialogue state, and thus plays a central role in task-oriented dialogue systems.

Dialogue Management Management +1

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