1 code implementation • 31 Mar 2024 • Dawei Zhu, Wenhao Wu, YiFan Song, Fangwei Zhu, Ziqiang Cao, Sujian Li
Due to the scarcity of annotated data, data augmentation is commonly used for training coherence evaluation models.
no code implementations • 22 Dec 2023 • Ruifeng Yuan, Shichao Sun, Zili Wang, Ziqiang Cao, Wenjie Li
It focuses on preserving the knowledge and experience from the history dialogue between the user and AI assistant, which can be applied to future dialogue for generating a better response.
1 code implementation • 20 Nov 2023 • Lei Geng, Xu Yan, Ziqiang Cao, Juntao Li, Wenjie Li, Sujian Li, Xinjie Zhou, Yang Yang, Jun Zhang
We achieve a biomedical multilingual corpus by incorporating three granularity knowledge alignments (entity, fact, and passage levels) into monolingual corpora.
1 code implementation • 16 Aug 2023 • Siqi Song, Qi Lv, Lei Geng, Ziqiang Cao, Guohong Fu
In this paper, we propose a retrieval-augmented spelling check framework called RSpell, which searches corresponding domain terms and incorporates them into CSC models.
1 code implementation • 23 May 2023 • Yang Bai, Min Cao, Daming Gao, Ziqiang Cao, Chen Chen, Zhenfeng Fan, Liqiang Nie, Min Zhang
RA offsets the overfitting risk by introducing a novel positive relation detection task (i. e., learning to distinguish strong and weak positive pairs).
Ranked #2 on Text based Person Retrieval on RSTPReid
no code implementations • 22 May 2023 • Yang Bai, Jingyao Wang, Min Cao, Chen Chen, Ziqiang Cao, Liqiang Nie, Min Zhang
Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description.
1 code implementation • 8 May 2023 • Zecheng Tang, Pinzheng Wang, Keyan Zhou, Juntao Li, Ziqiang Cao, Min Zhang
Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space.
no code implementations • 14 Mar 2023 • Min Cao, Yang Bai, Jingyao Wang, Ziqiang Cao, Liqiang Nie, Min Zhang
The proposed framework equipped with only two embedding layers achieves $O(1)$ querying time complexity, while improving the retrieval efficiency and keeping its performance, when applied prior to the common image-text retrieval methods.
no code implementations • 29 Nov 2022 • Ruifeng Yuan, Zili Wang, Ziqiang Cao, Wenjie Li
Drawn inspiration from prefix-tuning, we are allowed to integrate the task knowledge from text summarization and question answering into a properly designed prefix and apply the merged prefix to query-focused summarization.
no code implementations • 1 Nov 2022 • Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao, Ziqiang Cao, Sujian Li, Hua Wu
We first measure a model's factual robustness by its success rate to defend against adversarial attacks when generating factual information.
no code implementations • 24 Aug 2022 • Qi Lv, Ziqiang Cao, Wenrui Xie, Derui Wang, Jingwen Wang, Zhiwei Hu, Tangkun Zhang, Ba Yuan, Yuanhang Li, Min Cao, Wenjie Li, Sujian Li, Guohong Fu
Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model.
no code implementations • 22 Aug 2022 • Xu Yan, Chunhui Ai, Ziqiang Cao, Min Cao, Sujian Li, Wenjie Li, Guohong Fu
While the builders of existing image-text retrieval datasets strive to ensure that the caption matches the linked image, they cannot prevent a caption from fitting other images.
no code implementations • 15 Jun 2022 • Chunhui Ai, Derui Wang, Xu Yan, Yang Xu, Wenrui Xie, Ziqiang Cao
With so many articles of varying qualities being produced every moment, it is a very urgent task to screen outstanding articles and commit them to social media.
1 code implementation • 21 Mar 2022 • Qi Lv, Ziqiang Cao, Lei Geng, Chunhui Ai, Xu Yan, Guohong Fu
However, there is a big gap between the real input scenario and automatic generated corpus.
no code implementations • ACL 2021 • Wenhao Wu, Wei Li, Xinyan Xiao, Jiachen Liu, Ziqiang Cao, Sujian Li, Hua Wu, Haifeng Wang
Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.
no code implementations • 9 Nov 2018 • Yan-ran Li, Wenjie Li, Ziqiang Cao, Chengyao Chen
To sustain engaging conversation, it is critical for chatbots to make good use of relevant knowledge.
no code implementations • ACL 2018 • Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei
Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably.
Ranked #23 on Text Summarization on GigaWord
no code implementations • 13 Nov 2017 • Ziqiang Cao, Furu Wei, Wenjie Li, Sujian Li
While previous abstractive summarization approaches usually focus on the improvement of informativeness, we argue that faithfulness is also a vital prerequisite for a practical abstractive summarization system.
Ranked #22 on Text Summarization on GigaWord
13 code implementations • IJCNLP 2017 • Yan-ran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, Shuzi Niu
We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
no code implementations • 28 Nov 2016 • Ziqiang Cao, Chuwei Luo, Wenjie Li, Sujian Li
In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder.
no code implementations • 28 Nov 2016 • Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei
Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents.
no code implementations • COLING 2016 • Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei, Yan-ran Li
Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization.
no code implementations • 26 Nov 2015 • Ziqiang Cao, Chengyao Chen, Wenjie Li, Sujian Li, Furu Wei, Ming Zhou
Both informativeness and readability of the collected summaries are verified by manual judgment.
no code implementations • 8 Jul 2015 • Xiaojun Wan, Ziqiang Cao, Furu Wei, Sujian Li, Ming Zhou
However, according to our quantitative analysis, none of the existing summarization models can always produce high-quality summaries for different document sets, and even a summarization model with good overall performance may produce low-quality summaries for some document sets.