Search Results for author: Xiuying Chen

Found 40 papers, 19 papers with code

Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation

no code implementations Findings (EMNLP) 2021 Qingqing Zhu, Xiuying Chen, Pengfei Wu, Junfei Liu, Dongyan Zhao

Hence, in this paper, we introduce a combination of curriculum learning and knowledge distillation for efficient dialogue generation models, where curriculum learning can help knowledge distillation from data and model aspects.

Dialogue Generation Knowledge Distillation

Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding

no code implementations NAACL (GeBNLP) 2022 Xiuying Chen, Mingzhe Li, Rui Yan, Xin Gao, Xiangliang Zhang

Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases. However, debias on the Chinese language, one of the most spoken languages, has been less explored. Meanwhile, existing literature relies on manually created supplementary data, which is time- and energy-consuming. In this work, we propose the first Chinese Gender-neutral word Embedding model (CGE) based on Word2vec, which learns gender-neutral word embeddings without any labeled data. Concretely, CGE utilizes and emphasizes the rich feminine and masculine information contained in radicals, i. e., a kind of component in Chinese characters, during the training procedure. This consequently alleviates discriminative gender biases. Experimental results on public benchmark datasets show that our unsupervised method outperforms the state-of-the-art supervised debiased word embedding models without sacrificing the functionality of the embedding model.

Word Embeddings

Modeling non-uniform uncertainty in Reaction Prediction via Boosting and Dropout

no code implementations7 Oct 2023 Taicheng Guo, Changsheng Ma, Xiuying Chen, Bozhao Nan, Kehan Guo, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

With the widespread adoption of generative models, the Variational Autoencoder(VAE) framework has typically been employed to tackle challenges in reaction prediction, where the reactants are encoded as a condition for the decoder, which then generates the product.

Automated Bioinformatics Analysis via AutoBA

1 code implementation6 Sep 2023 Juexiao Zhou, Bin Zhang, Xiuying Chen, Haoyang Li, Xiaopeng Xu, Siyuan Chen, Xin Gao

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the analysis continues to grow.

Language Modelling Large Language Model

Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest Cost

1 code implementation19 Jun 2023 Juexiao Zhou, Xiuying Chen, Xin Gao

Medical artificial general intelligence (AGI) is an emerging field that aims to develop systems specifically designed for medical applications that possess the ability to understand, learn, and apply knowledge across a wide range of tasks and domains.

Improving the Robustness of Summarization Systems with Dual Augmentation

1 code implementation1 Jun 2023 Xiuying Chen, Guodong Long, Chongyang Tao, Mingzhe Li, Xin Gao, Chengqi Zhang, Xiangliang Zhang

The other factor is in the latent space, where the attacked inputs bring more variations to the hidden states.

Data Augmentation

UMSE: Unified Multi-scenario Summarization Evaluation

1 code implementation26 May 2023 Shen Gao, Zhitao Yao, Chongyang Tao, Xiuying Chen, Pengjie Ren, Zhaochun Ren, Zhumin Chen

Experimental results across three typical scenarios on the benchmark dataset SummEval indicate that our UMSE can achieve comparable performance with several existing strong methods which are specifically designed for each scenario.

Text Summarization

Interactive Natural Language Processing

no code implementations22 May 2023 Zekun Wang, Ge Zhang, Kexin Yang, Ning Shi, Wangchunshu Zhou, Shaochun Hao, Guangzheng Xiong, Yizhi Li, Mong Yuan Sim, Xiuying Chen, Qingqing Zhu, Zhenzhu Yang, Adam Nik, Qi Liu, Chenghua Lin, Shi Wang, Ruibo Liu, Wenhu Chen, Ke Xu, Dayiheng Liu, Yike Guo, Jie Fu

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence.

Decision Making

A Topic-aware Summarization Framework with Different Modal Side Information

no code implementations19 May 2023 Xiuying Chen, Mingzhe Li, Shen Gao, Xin Cheng, Qiang Yang, Qishen Zhang, Xin Gao, Xiangliang Zhang

To address these two challenges, we first propose a unified topic encoder, which jointly discovers latent topics from the document and various kinds of side information.

Contrastive Learning

Decouple knowledge from parameters for plug-and-play language modeling

1 code implementation19 May 2023 Xin Cheng, Yankai Lin, Xiuying Chen, Dongyan Zhao, Rui Yan

The key intuition is to decouple the knowledge storage from model parameters with an editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the DPM.

Domain Adaptation Language Modelling +1

Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory

1 code implementation3 May 2023 Xin Cheng, Di Luo, Xiuying Chen, Lemao Liu, Dongyan Zhao, Rui Yan

In this paper, by exploring the duality of the primal problem: better generation also prompts better memory, we propose a novel framework, selfmem, which addresses this limitation by iteratively employing a retrieval-augmented generator to create an unbounded memory pool and using a memory selector to choose one output as memory for the subsequent generation round.

Abstractive Text Summarization Dialogue Generation +2

SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model

1 code implementation21 Apr 2023 Juexiao Zhou, Xiaonan He, Liyuan Sun, Jiannan Xu, Xiuying Chen, Yuetan Chu, Longxi Zhou, Xingyu Liao, Bin Zhang, Xin Gao

Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population.

Language Modelling Large Language Model

Learning towards Selective Data Augmentation for Dialogue Generation

no code implementations17 Mar 2023 Xiuying Chen, Mingzhe Li, Jiayi Zhang, Xiaoqiang Xia, Chen Wei, Jianwei Cui, Xin Gao, Xiangliang Zhang, Rui Yan

As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples.

Data Augmentation Dialogue Generation +1

EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator

no code implementations3 Jan 2023 Mingzhe Li, Xiuying Chen, Weiheng Liao, Yang song, Tao Zhang, Dongyan Zhao, Rui Yan

The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource.

Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order

1 code implementation2 Jan 2023 Xiuying Chen, Mingzhe Li, Shen Gao, Zhangming Chan, Dongyan Zhao, Xin Gao, Xiangliang Zhang, Rui Yan

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline.

Document Summarization Timeline Summarization +1

Scientific Paper Extractive Summarization Enhanced by Citation Graphs

no code implementations8 Dec 2022 Xiuying Chen, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao, Xiangliang Zhang

We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task.

Extractive Summarization Link Prediction

Towards Improving Faithfulness in Abstractive Summarization

1 code implementation4 Oct 2022 Xiuying Chen, Mingzhe Li, Xin Gao, Xiangliang Zhang

The evaluation of factual consistency also shows that our model generates more faithful summaries than baselines.

Abstractive Text Summarization Language Modelling +1

Target-aware Abstractive Related Work Generation with Contrastive Learning

1 code implementation26 May 2022 Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao, Xiangliang Zhang

The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers.

Contrastive Learning TAG

Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation

1 code implementation ACL 2021 Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Xiangliang Zhang, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area.

Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension

1 code implementation14 Dec 2020 Xiuying Chen, Zhi Cui, Jiayi Zhang, Chen Wei, Jianwei Cui, Bin Wang, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog.

Multi-Task Learning Reading Comprehension +1

The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement

no code implementations14 Dec 2020 Mingzhe Li, Xiuying Chen, Min Yang, Shen Gao, Dongyan Zhao, Rui Yan

In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style.


Meaningful Answer Generation of E-Commerce Question-Answering

no code implementations14 Nov 2020 Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan

To generate more meaningful answers, in this paper, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration.

Answer Generation Question Answering +1

Learning to Respond with Your Favorite Stickers: A Framework of Unifying Multi-Modality and User Preference in Multi-Turn Dialog

no code implementations5 Nov 2020 Shen Gao, Xiuying Chen, Li Liu, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user.

VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles

1 code implementation EMNLP 2020 Mingzhe Li, Xiuying Chen, Shen Gao, Zhangming Chan, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem.

From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

no code implementations10 May 2020 Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan

Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words.

Text Summarization

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

1 code implementation10 Mar 2020 Shen Gao, Xiuying Chen, Chang Liu, Li Liu, Dongyan Zhao, Rui Yan

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances.

Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation

no code implementations IJCNLP 2019 Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan

Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.

Text Generation

RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search

no code implementations28 Oct 2019 Xiuying Chen, Daorui Xiao, Shen Gao, Guojun Liu, Wei. Lin, Bo Zheng, Dongyan Zhao, Rui Yan

Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM).

How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing

1 code implementation IJCNLP 2019 Shen Gao, Xiuying Chen, Piji Li, Zhangming Chan, Dongyan Zhao, Rui Yan

There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries.

Abstractive Text Summarization

Learning towards Abstractive Timeline Summarization

1 code implementation IJCAI 2019 2019 Xiuying Chen, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, Rui Yan

Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods. In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process. Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events. Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.

Document Summarization Timeline Summarization +2

Abstractive Text Summarization by Incorporating Reader Comments

no code implementations13 Dec 2018 Shen Gao, Xiuying Chen, Piji Li, Zhaochun Ren, Lidong Bing, Dongyan Zhao, Rui Yan

To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect.

Reader-Aware Summarization

Iterative Document Representation Learning Towards Summarization with Polishing

1 code implementation EMNLP 2018 Xiuying Chen, Shen Gao, Chongyang Tao, Yan Song, Dongyan Zhao, Rui Yan

In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents.

Extractive Text Summarization Representation Learning

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