Shengjie Li

Liaoning Shihua University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Vincent Ng

Hong Kong Polytechnic University

Matthew Stone

Rutgers, The State University of New Jersey

Qian Gong

Oak Ridge National Laboratory

Jiaming Wang

Wuhan Institute of Technology

Piyush Sharma

Sharda University

Vincent Ng

The University of Texas at Dallas

Guodong Sun

Hubei University of Technology

Zuoqiang Du

Hefei University of Technology

Hideo Kobayashi

Saiseikai Ibaraki Hospital

Malihe Alikhani

Northeastern University

Cooperative Institutions

Sun Yat-sen University

Shenzhen Polytechnic

Nanjing University

Central South University

Guizhou University

Jingdong (China)

Shandong Normal University

University of North Texas at Dallas

China University of Petroleum, East China

Tokyo Institute of Technology

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Variational quantum algorithms for trace norms and their applications

Communications in Theoretical Physics (2021)

Shengjie Li Jin‐Min Liang Shu‐Qian Shen Ming Li

The trace norm of matrices plays an important role in quantum information and quantum computing. How to quantify it in today's noisy intermediate scale quantum (NISQ) devices is a crucial task for information processing. In this paper, we present three variational quantum algorithms on NISQ devices to estimate the trace norms corresponding to different situations. Compared with the previous methods, our means greatly reduce the requirement for quantum resources. Numerical experiments are provided to illustrate the effectiveness of our algorithms.

TRACE (psycholinguistics)

10.1088/1572-9494/ac1938

Cite

Citations (8)

The CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis Resolution in Dialogue: A Cross-Team Analysis

Shengjie Li Hideo Kobayashi Vincent Ng

The CODI-CRAC 2021 shared task is the first shared task that focuses exclusively on anaphora resolution in dialogue and provides three tracks, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. We perform a cross-task analysis of the systems that participated in the shared task in each of these tracks.

Coreference

Deixis

Anaphora (linguistics)

10.18653/v1/2021.codi-sharedtask.8

Cite

Citations (3)

Neural Anaphora Resolution in Dialogue

Hideo Kobayashi Shengjie Li Vincent Ng

We describe the systems that we developed for the three tracks of the CODI-CRAC 2021 shared task, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. Our team ranked second for entity coreference resolution, first for bridging resolution, and first for discourse deixis resolution.

Coreference

Deixis

Anaphora (linguistics)

Source

Cite

Citations (2)

Negative Guided Abstractive Dialogue Summarization

Interspeech 2022 (2022)

Junpeng Liu Yanyan Zou Yuxuan Xi Shengjie Li Mian Ma

10.21437/interspeech.2022-10395

Cite

Citations (0)

End-to-End Neural Discourse Deixis Resolution in Dialogue

arXiv (Cornell University) (2022)

Shengjie Li Vincent Ng

We adapt Lee et al.'s (2018) span-based entity coreference model to the task of end-to-end discourse deixis resolution in dialogue, specifically by proposing extensions to their model that exploit task-specific characteristics. The resulting model, dd-utt, achieves state-of-the-art results on the four datasets in the CODI-CRAC 2021 shared task.

Deixis

Coreference

End-to-end principle

10.48550/arxiv.2211.15980

Cite

Citations (0)

Automated Essay Scoring: A Reflection on the State of the Art

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2024)

Shengjie Li Vincent Ng

Reflection

10.18653/v1/2024.emnlp-main.991

Cite

Citations (0)

Clue: Cross-modal Coherence Modeling for Caption Generation

arXiv (Cornell University) (2020)

Malihe Alikhani Piyush Sharma Shengjie Li Radu Soricut Matthew Stone

We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning. Using an annotation protocol specifically devised for capturing image--caption coherence relations, we annotate 10,000 instances from publicly-available image--caption pairs. We introduce a new task for learning inferences in imagery and text, coherence relation prediction, and show that these coherence annotations can be exploited to learn relation classifiers as an intermediary step, and also train coherence-aware, controllable image captioning models. The results show a dramatic improvement in the consistency and quality of the generated captions with respect to information needs specified via coherence relations.

Closed captioning

Source

Cite

Citations (8)

A Novel LSTM-KAN Networks for Demand Forecasting in Manufacturing

Qian Gong Yuanfei Xue Rui Liu Zuoqiang Du Shengjie Li

10.1109/icbase63199.2024.10762575

Cite

Citations (0)

Cross-modal Coherence Modeling for Caption Generation

Malihe Alikhani Piyush Sharma Shengjie Li Radu Soricut Matthew Stone

We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning. Using an annotation protocol specifically devised for capturing image–caption coherence relations, we annotate 10,000 instances from publicly-available image–caption pairs. We introduce a new task for learning inferences in imagery and text, coherence relation prediction, and show that these coherence annotations can be exploited to learn relation classifiers as an intermediary step, and also train coherence-aware, controllable image captioning models. The results show a dramatic improvement in the consistency and quality of the generated captions with respect to information needs specified via coherence relations.

Closed captioning

10.18653/v1/2020.acl-main.583

Cite

Citations (43)

Improving memory-based collaborative filtering by incorporating trust and item-popularity

WIT transactions on engineering sciences (2014)

Juan Li Shengjie Li Qian Zhou

Popularity

10.2495/mesi141892

Cite

Citations (0)