Search Results for author: Xiaoqian Liu

Found 13 papers, 3 papers with code

Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges

no code implementations29 Dec 2023 Xiaoqian Liu, Jianbin Jiao, Junge Zhang

Decision-making is a dynamic process requiring perception, memory, and reasoning to make choices and find optimal policies.

Decision Making Few-Shot Learning

Benchmarking Continual Learning from Cognitive Perspectives

no code implementations6 Dec 2023 Xiaoqian Liu, Junge Zhang, Mingyi Zhang, Peipei Yang

To address these issues, we propose to integrate model cognitive capacities and evaluation metrics into a unified evaluation paradigm.

Benchmarking Continual Learning

Bridging the Gaps of Both Modality and Language: Synchronous Bilingual CTC for Speech Translation and Speech Recognition

1 code implementation21 Sep 2023 Chen Xu, Xiaoqian Liu, Erfeng He, Yuhao Zhang, Qianqian Dong, Tong Xiao, Jingbo Zhu, Dapeng Man, Wu Yang

In this study, we present synchronous bilingual Connectionist Temporal Classification (CTC), an innovative framework that leverages dual CTC to bridge the gaps of both modality and language in the speech translation (ST) task.

speech-recognition Speech Recognition +1

CTC-based Non-autoregressive Speech Translation

1 code implementation27 May 2023 Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu

Combining end-to-end speech translation (ST) and non-autoregressive (NAR) generation is promising in language and speech processing for their advantages of less error propagation and low latency.

Translation

Bridging the Granularity Gap for Acoustic Modeling

1 code implementation27 May 2023 Chen Xu, Yuhao Zhang, Chengbo Jiao, Xiaoqian Liu, Chi Hu, Xin Zeng, Tong Xiao, Anxiang Ma, Huizhen Wang, Jingbo Zhu

While Transformer has become the de-facto standard for speech, modeling upon the fine-grained frame-level features remains an open challenge of capturing long-distance dependencies and distributing the attention weights.

speech-recognition Speech Recognition

A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion

no code implementations27 Apr 2023 Xiaoqian Liu, Xu Han, Eric C. Chi, Boaz Nadler

In 1-bit matrix completion, the aim is to estimate an underlying low-rank matrix from a partial set of binary observations.

Low-Rank Matrix Completion

The NiuTrans End-to-End Speech Translation System for IWSLT 2021 Offline Task

no code implementations ACL (IWSLT) 2021 Chen Xu, Xiaoqian Liu, Xiaowen Liu, Laohu Wang, Canan Huang, Tong Xiao, Jingbo Zhu

This paper describes the submission of the NiuTrans end-to-end speech translation system for the IWSLT 2021 offline task, which translates from the English audio to German text directly without intermediate transcription.

Position Translation

Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models

no code implementations Findings of the Association for Computational Linguistics 2020 Zhiyuan Zhang, Xiaoqian Liu, Yi Zhang, Qi Su, Xu sun, Bin He

Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem.

Knowledge Graph Embedding World Knowledge

Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach

no code implementations26 May 2020 Jia Xue, Junxiang Chen, Ran Hu, Chen Chen, Chengda Zheng, Xiaoqian Liu, Tingshao Zhu

Across all identified topics, the dominant sentiments for the spread of coronavirus are anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics.

BIG-bench Machine Learning

HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network

no code implementations10 Nov 2019 Deli Chen, Xiaoqian Liu, Yankai Lin, Peng Li, Jie zhou, Qi Su, Xu sun

To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label.

General Classification Node Classification

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