Search Results for author: Xingshan Zeng

Found 15 papers, 5 papers with code

Prior Knowledge and Memory Enriched Transformer for Sign Language Translation

no code implementations Findings (ACL) 2022 Tao Jin, Zhou Zhao, Meng Zhang, Xingshan Zeng

This paper attacks the challenging problem of sign language translation (SLT), which involves not only visual and textual understanding but also additional prior knowledge learning (i. e. performing style, syntax).

POS Sign Language Translation +1

Multilingual Speech Translation with Unified Transformer: Huawei Noah’s Ark Lab at IWSLT 2021

no code implementations ACL (IWSLT) 2021 Xingshan Zeng, Liangyou Li, Qun Liu

We use a unified transformer architecture for our MultiST model, so that the data from different modalities (i. e., speech and text) and different tasks (i. e., Speech Recognition, Machine Translation, and Speech Translation) can be exploited to enhance the model’s ability.

Data Augmentation Machine Translation +3

SimulSLT: End-to-End Simultaneous Sign Language Translation

no code implementations8 Dec 2021 Aoxiong Yin, Zhou Zhao, Jinglin Liu, Weike Jin, Meng Zhang, Xingshan Zeng, Xiaofei He

Sign language translation as a kind of technology with profound social significance has attracted growing researchers' interest in recent years.

Sign Language Translation Translation

Neural News Recommendation with Collaborative News Encoding and Structural User Encoding

1 code implementation Findings (EMNLP) 2021 Zhiming Mao, Xingshan Zeng, Kam-Fai Wong

In this work, we propose a news recommendation framework consisting of collaborative news encoding (CNE) and structural user encoding (SUE) to enhance news and user representation learning.

News Recommendation Reading Comprehension +1

SimulLR: Simultaneous Lip Reading Transducer with Attention-Guided Adaptive Memory

no code implementations31 Aug 2021 Zhijie Lin, Zhou Zhao, Haoyuan Li, Jinglin Liu, Meng Zhang, Xingshan Zeng, Xiaofei He

Lip reading, aiming to recognize spoken sentences according to the given video of lip movements without relying on the audio stream, has attracted great interest due to its application in many scenarios.

Frame Lip Reading

RealTranS: End-to-End Simultaneous Speech Translation with Convolutional Weighted-Shrinking Transformer

no code implementations Findings (ACL) 2021 Xingshan Zeng, Liangyou Li, Qun Liu

To bridge the modality gap between speech and text, RealTranS gradually downsamples the input speech with interleaved convolution and unidirectional Transformer layers for acoustic modeling, and then maps speech features into text space with a weighted-shrinking operation and a semantic encoder.

Translation

Multilingual Speech Translation with Unified Transformer: Huawei Noah's Ark Lab at IWSLT 2021

no code implementations1 Jun 2021 Xingshan Zeng, Liangyou Li, Qun Liu

We use a unified transformer architecture for our MultiST model, so that the data from different modalities (i. e., speech and text) and different tasks (i. e., Speech Recognition, Machine Translation, and Speech Translation) can be exploited to enhance the model's ability.

Data Augmentation Machine Translation +3

Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations

1 code implementation ACL 2021 Lingzhi Wang, Xingshan Zeng, Kam-Fai Wong

To help individuals express themselves better, quotation recommendation is receiving growing attention.

Dynamic Online Conversation Recommendation

no code implementations ACL 2020 Xingshan Zeng, Jing Li, Lu Wang, Zhiming Mao, Kam-Fai Wong

Trending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner.

Neural Conversation Recommendation with Online Interaction Modeling

no code implementations IJCNLP 2019 Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong

The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis.

Collaborative Filtering

Joint Effects of Context and User History for Predicting Online Conversation Re-entries

1 code implementation ACL 2019 Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong

We hypothesize that both the context of the ongoing conversations and the users' previous chatting history will affect their continued interests in future engagement.

Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse

no code implementations NAACL 2018 Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong

We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics.

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