Search Results for author: Tao Gui

Found 43 papers, 26 papers with code

Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks

1 code implementation COLING 2022 Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu

Specifically, we re-formulate both token and sentence classification tasks into a unified language modeling task, and map label spaces of different tasks into the same vocabulary space.

Language Modelling Sentence Classification +1

CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation

1 code implementation ACL 2022 Zichu Fei, Qi Zhang, Tao Gui, Di Liang, Sirui Wang, Wei Wu, Xuanjing Huang

CQG employs a simple method to generate the multi-hop questions that contain key entities in multi-hop reasoning chains, which ensure the complexity and quality of the questions.

Question Generation Question-Generation

Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER

no code implementations COLING 2022 Jun Zhao, Xin Zhao, WenYu Zhan, Tao Gui, Qi Zhang, Liang Qiao, Zhanzhan Cheng, ShiLiang Pu

To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document.


PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack

no code implementations COLING 2022 Rui Zheng, Rong Bao, Qin Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu

To reduce the potential side effects of using defense modules, we further propose a novel forgetting restricted adversarial training, which filters out bad adversarial examples that impair the performance of original ones.

Adversarial Attack Domain Adaptation +2

LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases

no code implementations COLING 2022 Zichu Fei, Xin Zhou, Tao Gui, Qi Zhang, Xuanjing Huang

Existing KBQG models still face two main challenges: (1) Most models often focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph.

Natural Questions Question Generation +1

Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement

1 code implementation23 May 2023 Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Tao Gui, Qi Zhang, Xuanjing Huang

For example, with Text-davinci-003, our method boosts the performance of standard few-shot prompting by $8. 0\%$ on GSM8K and $17. 8\%$ on MultiArith; it also improves the performance of CoT by $6. 0\%$ on GSM8K and $6. 0\%$ on MathQA, respectively.


A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition

no code implementations21 May 2023 Limao Xiong, Jie zhou, Qunxi Zhu, Xiao Wang, Yuanbin Wu, Qi Zhang, Tao Gui, Xuanjing Huang, Jin Ma, Ying Shan

Particularly, we propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER.

named-entity-recognition Named Entity Recognition +2

Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization

1 code implementation20 May 2023 Ting Wu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

Models trained with empirical risk minimization (ERM) are revealed to easily rely on spurious correlations, resulting in poor generalization.

Out-of-Distribution Generalization text-classification +1

A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

no code implementations18 Mar 2023 Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang shen, Jie zhou, Siming Chen, Tao Gui, Qi Zhang, Xuanjing Huang

GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities.

Natural Language Understanding

How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks

no code implementations1 Mar 2023 Xuanting Chen, Junjie Ye, Can Zu, Nuo Xu, Rui Zheng, Minlong Peng, Jie zhou, Tao Gui, Qi Zhang, Xuanjing Huang

The GPT-3. 5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities.

Natural Language Inference Natural Language Understanding +1

Correspondence Transformers With Asymmetric Feature Learning and Matching Flow Super-Resolution

no code implementations CVPR 2023 Yixuan Sun, Dongyang Zhao, Zhangyue Yin, Yiwen Huang, Tao Gui, Wenqiang Zhang, Weifeng Ge

The asymmetric feature learning module exploits a biased cross-attention mechanism to encode token features of source images with their target counterparts.


Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?

1 code implementation21 Dec 2022 Ningyu Xu, Tao Gui, Ruotian Ma, Qi Zhang, Jingting Ye, Menghan Zhang, Xuanjing Huang

We demonstrate that the distance between the distributions of different languages is highly consistent with the syntactic difference in terms of linguistic formalisms.

Zero-Shot Cross-Lingual Transfer

Efficient Adversarial Training with Robust Early-Bird Tickets

1 code implementation14 Nov 2022 Zhiheng Xi, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

Adversarial training is one of the most powerful methods to improve the robustness of pre-trained language models (PLMs).

Towards Understanding Omission in Dialogue Summarization

1 code implementation14 Nov 2022 Yicheng Zou, Kaitao Song, Xu Tan, Zhongkai Fu, Qi Zhang, Dongsheng Li, Tao Gui

By analyzing this dataset, we find that a large improvement in summarization quality can be achieved by providing ground-truth omission labels for the summarization model to recover omission information, which demonstrates the importance of omission detection for omission mitigation in dialogue summarization.

Robust Lottery Tickets for Pre-trained Language Models

1 code implementation ACL 2022 Rui Zheng, Rong Bao, Yuhao Zhou, Di Liang, Sirui Wang, Wei Wu, Tao Gui, Qi Zhang, Xuanjing Huang

Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching accuracy comparable to the original models.

Adversarial Robustness

Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER

no code implementations10 Oct 2022 Ruotian Ma, Xuanting Chen, Lin Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

As the categories of named entities rapidly increase in real-world applications, class-incremental learning for NER is in demand, which continually learns new entity classes while maintaining the old knowledge.

class-incremental learning Class Incremental Learning +3

Less is Better: Recovering Intended-Feature Subspace to Robustify NLU Models

1 code implementation COLING 2022 Ting Wu, Tao Gui

When delving into a lower manifold to remove redundancies, RISK reveals that an extremely low-dimensional subspace with intended features can robustly represent the highly biased dataset.

Causal Intervention Improves Implicit Sentiment Analysis

no code implementations COLING 2022 Siyin Wang, Jie zhou, Changzhi Sun, Junjie Ye, Tao Gui, Qi Zhang, Xuanjing Huang

In this work, we propose a causal intervention model for Implicit Sentiment Analysis using Instrumental Variable (ISAIV).

Sentiment Analysis

Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents

1 code implementation Findings (ACL) 2022 Yicheng Zou, Hongwei Liu, Tao Gui, Junzhe Wang, Qi Zhang, Meng Tang, Haixiang Li, Daniel Wang

Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation.

Community Question Answering Information Retrieval +1

Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models

no code implementations14 Oct 2021 Xin Zhou, Ruotian Ma, Tao Gui, Yiding Tan, Qi Zhang, Xuanjing Huang

Specifically, for each task, a label word set is first constructed by selecting a high-frequency word for each class respectively, and then, task-specific vectors are inserted into the inputs and optimized to manipulate the model predictions towards the corresponding label words.

Language Modelling Text Generation

Template-free Prompt Tuning for Few-shot NER

1 code implementation NAACL 2022 Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Linyang Li, Qi Zhang, Xuanjing Huang

Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words.

Few-Shot Learning Few-shot NER

A Relation-Oriented Clustering Method for Open Relation Extraction

1 code implementation EMNLP 2021 Jun Zhao, Tao Gui, Qi Zhang, Yaqian Zhou

The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE).

Relation Extraction

Heterogeneous Graph Neural Networks for Keyphrase Generation

1 code implementation EMNLP 2021 Jiacheng Ye, Ruijian Cai, Tao Gui, Qi Zhang

The encoder-decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not.

Keyphrase Generation

Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining

1 code implementation EMNLP 2021 Yicheng Zou, Bolin Zhu, Xingwu Hu, Tao Gui, Qi Zhang

With the rapid increase in the volume of dialogue data from daily life, there is a growing demand for dialogue summarization.

SENT: Sentence-level Distant Relation Extraction via Negative Training

1 code implementation ACL 2021 Ruotian Ma, Tao Gui, Linyang Li, Qi Zhang, Yaqian Zhou, Xuanjing Huang

In this work, we propose the use of negative training (NT), in which a model is trained using complementary labels regarding that ``the instance does not belong to these complementary labels".

Relation Extraction

A Unified Generative Framework for Various NER Subtasks

1 code implementation ACL 2021 Hang Yan, Tao Gui, Junqi Dai, Qipeng Guo, Zheng Zhang, Xipeng Qiu

To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework.

named-entity-recognition Named Entity Recognition +2

One2Set: Generating Diverse Keyphrases as a Set

1 code implementation ACL 2021 Jiacheng Ye, Tao Gui, Yichao Luo, Yige Xu, Qi Zhang

In this work, we propose a new training paradigm One2Set without predefining an order to concatenate the keyphrases.

Keyphrase Generation

Uncertainty-Aware Label Refinement for Sequence Labeling

1 code implementation EMNLP 2020 Tao Gui, Jiacheng Ye, Qi Zhang, Zhengyan Li, Zichu Fei, Yeyun Gong, Xuanjing Huang

Conditional random fields (CRF) for label decoding has become ubiquitous in sequence labeling tasks.

Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features

1 code implementation18 Nov 2019 Tao Gui, Lizhi Qing, Qi Zhang, Jiacheng Ye, HangYan, Zichu Fei, Xuanjing Huang

In order to effectively reduce the impact of non-ideal auxiliary tasks on the main task, we further proposed a novel meta-learning-based multi-task learning approach, which trained the shared hidden layers on auxiliary tasks, while the meta-optimization objective was to minimize the loss on the main task, ensuring that the optimizing direction led to an improvement on the main task.

Data Augmentation General Classification +3

A Lexicon-Based Graph Neural Network for Chinese NER

no code implementations IJCNLP 2019 Tao Gui, Yicheng Zou, Qi Zhang, Minlong Peng, Jinlan Fu, Zhongyu Wei, Xuanjing Huang

Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success.

Chinese Named Entity Recognition named-entity-recognition +2

Switch-LSTMs for Multi-Criteria Chinese Word Segmentation

no code implementations19 Dec 2018 Jingjing Gong, Xinchi Chen, Tao Gui, Xipeng Qiu

With these auto-switched LSTMs, our model provides a more flexible solution for multi-criteria CWS, which is also easy to transfer the learned knowledge to new criteria.

Chinese Word Segmentation

Long Short-Term Memory with Dynamic Skip Connections

1 code implementation9 Nov 2018 Tao Gui, Qi Zhang, Lujun Zhao, Yaosong Lin, Minlong Peng, Jingjing Gong, Xuanjing Huang

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length.

Named Entity Recognition (NER) Sentiment Analysis

Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging

no code implementations EMNLP 2018 Tao Gui, Qi Zhang, Jingjing Gong, Minlong Peng, Di Liang, Keyu Ding, Xuanjing Huang

However, from a linguistic perspective, Twitter users not only tend to mimic the formal expressions of traditional media, like news, but they also appear to be developing linguistically informal styles.

Domain Adaptation Multi-Task Learning +3

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