Search Results for author: Xingshan Zeng

Found 32 papers, 13 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).

Decoder POS +3

End-to-End Simultaneous Speech Translation with Pretraining and Distillation: Huawei Noah’s System for AutoSimTranS 2022

no code implementations NAACL (AutoSimTrans) 2022 Xingshan Zeng, Pengfei Li, Liangyou Li, Qun Liu

This paper describes the system submitted to AutoSimTrans 2022 from Huawei Noah’s Ark Lab, which won the first place in the audio input track of the Chinese-English translation task.

Decoder Knowledge Distillation +2

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 Decoder +5

Chain-of-Probe: Examing the Necessity and Accuracy of CoT Step-by-Step

no code implementations23 Jun 2024 Zezhong Wang, Xingshan Zeng, Weiwen Liu, YuFei Wang, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong

To address these questions, we propose a method, namely Chain-of-Probe (CoP), to probe changes in the mind during the model's reasoning.

valid

Learning to Edit: Aligning LLMs with Knowledge Editing

1 code implementation19 Feb 2024 Yuxin Jiang, YuFei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang

Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention.

knowledge editing Philosophy

Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models

no code implementations8 Feb 2024 Lingzhi Wang, Xingshan Zeng, Jinsong Guo, Kam-Fai Wong, Georg Gottlob

The aim of this study is to investigate Machine Unlearning (MU), a burgeoning field focused on addressing concerns related to neural models inadvertently retaining personal or sensitive data.

Computational Efficiency Language Modelling +1

MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models

1 code implementation30 Jan 2024 Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang, YuFei Wang, Liangyou Li, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong

Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications.

Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios

1 code implementation30 Jan 2024 Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu

The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools.

Benchmarking

FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models

1 code implementation31 Oct 2023 Yuxin Jiang, YuFei Wang, Xingshan Zeng, Wanjun Zhong, Liangyou Li, Fei Mi, Lifeng Shang, Xin Jiang, Qun Liu, Wei Wang

To fill this research gap, in this paper, we propose FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for LLMs.

Instruction Following

Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis

no code implementations9 Oct 2023 Jianqiao Lu, Wenyong Huang, Nianzu Zheng, Xingshan Zeng, Yu Ting Yeung, Xiao Chen

For SLU, LaSyn improves our E2E baseline by absolute 4. 1% for intent classification accuracy and 3. 8% for slot filling SLU-F1 on SLURP, and absolute 4. 49% and 2. 25% for exact match (EM) and EM-Tree accuracies on STOP respectively.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +6

SELF: Self-Evolution with Language Feedback

no code implementations1 Oct 2023 Jianqiao Lu, Wanjun Zhong, Wenyong Huang, YuFei Wang, Qi Zhu, Fei Mi, Baojun Wang, Weichao Wang, Xingshan Zeng, Lifeng Shang, Xin Jiang, Qun Liu

SELF initiates with a meta-skill learning process that equips the LLMs with capabilities for self-feedback and self-refinement.

Language Modelling Large Language Model

Aligning Large Language Models with Human: A Survey

1 code implementation24 Jul 2023 YuFei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, Qun Liu

(2) Training methodologies: a detailed review of the prevailing training methods employed for LLM alignment.

KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment

1 code implementation11 May 2023 Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, Hongzhi Yin

Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set.

Machine Unlearning Response Generation

Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation

no code implementations27 Feb 2023 Lingzhi Wang, Mrinmaya Sachan, Xingshan Zeng, Kam-Fai Wong

Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog.

Response Generation

AdaTranS: Adapting with Boundary-based Shrinking for End-to-End Speech Translation

no code implementations17 Dec 2022 Xingshan Zeng, Liangyou Li, Qun Liu

To alleviate the data scarcity problem in End-to-end speech translation (ST), pre-training on data for speech recognition and machine translation is considered as an important technique.

Machine Translation speech-recognition +2

DIGAT: Modeling News Recommendation with Dual-Graph Interaction

1 code implementation11 Oct 2022 Zhiming Mao, Jian Li, Hongru Wang, Xingshan Zeng, Kam-Fai Wong

Second, existing graph-based NR methods are promising but lack effective news-user feature interaction, rendering the graph-based recommendation suboptimal.

Graph Attention News Recommendation +1

Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge

no code implementations23 Sep 2022 Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong

In addition to conducting experiments on a popular dataset (ReDial), we also include a multi-domain dataset (OpenDialKG) to show the effectiveness of our model.

Recommendation Systems

MLSLT: Towards Multilingual Sign Language Translation

no code implementations CVPR 2022 Aoxiong Yin, Zhou Zhao, Weike Jin, Meng Zhang, Xingshan Zeng, Xiaofei He

In addition, we also explore zero-shot translation in sign language and find that our model can achieve comparable performance to the supervised BSLT model on some language pairs.

Sign Language Translation Translation

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.

Decoder Sign Language Translation +1

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.

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 Decoder +5

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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