Search Results for author: Xiaozhi Wang

Found 35 papers, 28 papers with code

Event-level Knowledge Editing

1 code implementation20 Feb 2024 Hao Peng, Xiaozhi Wang, Chunyang Li, Kaisheng Zeng, Jiangshan Duo, Yixin Cao, Lei Hou, Juanzi Li

However, natural knowledge updates in the real world come from the occurrences of new events rather than direct changes in factual triplets.

knowledge editing

MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation

1 code implementation15 Nov 2023 Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie zhou, Juanzi Li

Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships.

Event Argument Extraction Event Detection +3

When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks

no code implementations15 Nov 2023 Hao Peng, Xiaozhi Wang, Jianhui Chen, Weikai Li, Yunjia Qi, Zimu Wang, Zhili Wu, Kaisheng Zeng, Bin Xu, Lei Hou, Juanzi Li

In this paper, we find that ICL falls short of handling specification-heavy tasks, which are tasks with complicated and extensive task specifications, requiring several hours for ordinary humans to master, such as traditional information extraction tasks.

In-Context Learning

Finding and Editing Multi-Modal Neurons in Pre-Trained Transformer

no code implementations13 Nov 2023 Haowen Pan, Yixin Cao, Xiaozhi Wang, Xun Yang

Multi-modal large language models (LLM) have achieved powerful capabilities for visual semantic understanding in recent years.

knowledge editing

Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment

no code implementations16 Oct 2023 Ji Qi, Kaixuan Ji, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Lei Hou, Juanzi Li, Bin Xu

Open Information Extraction (OIE) aims to extract objective structured knowledge from natural texts, which has attracted growing attention to build dedicated models with human experience.

In-Context Learning Open Information Extraction

OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding

1 code implementation25 Sep 2023 Hao Peng, Xiaozhi Wang, Feng Yao, Zimu Wang, Chuzhao Zhu, Kaisheng Zeng, Lei Hou, Juanzi Li

Event understanding aims at understanding the content and relationship of events within texts, which covers multiple complicated information extraction tasks: event detection, event argument extraction, and event relation extraction.

Event Argument Extraction Event Detection +2

The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation

1 code implementation12 Jun 2023 Hao Peng, Xiaozhi Wang, Feng Yao, Kaisheng Zeng, Lei Hou, Juanzi Li, Zhiyuan Liu, Weixing Shen

In this paper, we check the reliability of EE evaluations and identify three major pitfalls: (1) The data preprocessing discrepancy makes the evaluation results on the same dataset not directly comparable, but the data preprocessing details are not widely noted and specified in papers.

Event Argument Extraction Event Detection +1

Emergent Modularity in Pre-trained Transformers

1 code implementation28 May 2023 Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Chaojun Xiao, Xiaozhi Wang, Xu Han, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Jie zhou

In analogy to human brains, we consider two main characteristics of modularity: (1) functional specialization of neurons: we evaluate whether each neuron is mainly specialized in a certain function, and find that the answer is yes.

Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction

1 code implementation23 May 2023 Ji Qi, Chuchun Zhang, Xiaozhi Wang, Kaisheng Zeng, Jifan Yu, Jinxin Liu, Jiuding Sun, Yuxiang Chen, Lei Hou, Juanzi Li, Bin Xu

In this paper, we present the first benchmark that simulates the evaluation of open information extraction models in the real world, where the syntactic and expressive distributions under the same knowledge meaning may drift variously.

Language Modelling Large Language Model +1

ChatLog: Recording and Analyzing ChatGPT Across Time

1 code implementation27 Apr 2023 Shangqing Tu, Chunyang Li, Jifan Yu, Xiaozhi Wang, Lei Hou, Juanzi Li

While there are abundant researches about evaluating ChatGPT on natural language understanding and generation tasks, few studies have investigated how ChatGPT's behavior changes over time.

Natural Language Understanding

GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation

1 code implementation26 Mar 2023 Ji Qi, Jifan Yu, Teng Tu, Kunyu Gao, Yifan Xu, Xinyu Guan, Xiaozhi Wang, Yuxiao Dong, Bin Xu, Lei Hou, Juanzi Li, Jie Tang, Weidong Guo, Hui Liu, Yu Xu

Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i. e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative.

Video Captioning

Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion Inference

1 code implementation19 Feb 2023 Ming Li, Yusheng Su, Hsiu-Yuan Huang, Jiali Cheng, Xin Hu, Xinmiao Zhang, Huadong Wang, Yujia Qin, Xiaozhi Wang, Kristen A. Lindquist, Zhiyuan Liu, Dan Zhang

Humans no doubt use language to communicate about their emotional experiences, but does language in turn help humans understand emotions, or is language just a vehicle of communication?

Attribute Language Modelling

READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises

1 code implementation14 Feb 2023 Chenglei Si, Zhengyan Zhang, Yingfa Chen, Xiaozhi Wang, Zhiyuan Liu, Maosong Sun

In order to fill this important gap, we construct READIN: a Chinese multi-task benchmark with REalistic And Diverse Input Noises.

Data Augmentation Fairness +2

Finding Skill Neurons in Pre-trained Transformer-based Language Models

1 code implementation14 Nov 2022 Xiaozhi Wang, Kaiyue Wen, Zhengyan Zhang, Lei Hou, Zhiyuan Liu, Juanzi Li

Furthermore, we demonstrate the skill neurons are most likely generated in pre-training rather than fine-tuning by showing that the skill neurons found with prompt tuning are also crucial for other fine-tuning methods freezing neuron weights, such as the adapter-based tuning and BitFit.

Network Pruning

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

1 code implementation14 Nov 2022 Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie zhou

It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude.

Event Relation Extraction Relation +1

LEVEN: A Large-Scale Chinese Legal Event Detection Dataset

1 code implementation Findings (ACL) 2022 Feng Yao, Chaojun Xiao, Xiaozhi Wang, Zhiyuan Liu, Lei Hou, Cunchao Tu, Juanzi Li, Yun Liu, Weixing Shen, Maosong Sun

However, existing Legal Event Detection (LED) datasets only concern incomprehensive event types and have limited annotated data, which restricts the development of LED methods and their downstream applications.

Event Detection Retrieval

Doppler velocity-based algorithm for Clustering and Velocity Estimation of moving objects

no code implementations24 Dec 2021 Mian Guo, Kai Zhong, Xiaozhi Wang

Then we estimate the velocity of the moving objects using the estimated LiDAR velocity and the Doppler velocity of moving objects obtained by clustering.

Autonomous Driving Clustering

On Transferability of Prompt Tuning for Natural Language Processing

1 code implementation NAACL 2022 Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie zhou

To explore whether we can improve PT via prompt transfer, we empirically investigate the transferability of soft prompts across different downstream tasks and PLMs in this work.

Natural Language Understanding Transfer Learning

Exploring Universal Intrinsic Task Subspace via Prompt Tuning

1 code implementation15 Oct 2021 Yujia Qin, Xiaozhi Wang, Yusheng Su, Yankai Lin, Ning Ding, Jing Yi, Weize Chen, Zhiyuan Liu, Juanzi Li, Lei Hou, Peng Li, Maosong Sun, Jie zhou

In the experiments, we study diverse few-shot NLP tasks and surprisingly find that in a 250-dimensional subspace found with 100 tasks, by only tuning 250 free parameters, we can recover 97% and 83% of the full prompt tuning performance for 100 seen tasks (using different training data) and 20 unseen tasks, respectively, showing great generalization ability of the found intrinsic task subspace.

Sub-Character Tokenization for Chinese Pretrained Language Models

2 code implementations1 Jun 2021 Chenglei Si, Zhengyan Zhang, Yingfa Chen, Fanchao Qi, Xiaozhi Wang, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun

2) Pronunciation-based SubChar tokenizers can encode Chinese homophones into the same transliteration sequences and produce the same tokenization output, hence being robust to homophone typos.

Chinese Word Segmentation Computational Efficiency +2

Neural Gibbs Sampling for Joint Event Argument Extraction

1 code implementation Asian Chapter of the Association for Computational Linguistics 2020 Xiaozhi Wang, Shengyu Jia, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Jie zhou

Existing EAE methods either extract each event argument roles independently or sequentially, which cannot adequately model the joint probability distribution among event arguments and their roles.

Event Argument Extraction Event Extraction

Train No Evil: Selective Masking for Task-Guided Pre-Training

1 code implementation EMNLP 2020 Yuxian Gu, Zhengyan Zhang, Xiaozhi Wang, Zhiyuan Liu, Maosong Sun

In this stage, the model is trained by masked language modeling on in-domain unsupervised data to learn domain-specific patterns and we propose a novel selective masking strategy to learn task-specific patterns.

Language Modelling Masked Language Modeling +1

Adversarial Language Games for Advanced Natural Language Intelligence

no code implementations5 Nov 2019 Yuan Yao, Haoxi Zhong, Zhengyan Zhang, Xu Han, Xiaozhi Wang, Chaojun Xiao, Guoyang Zeng, Zhiyuan Liu, Maosong Sun

In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word.

Board Games

HMEAE: Hierarchical Modular Event Argument Extraction

1 code implementation IJCNLP 2019 Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie zhou, Xiang Ren

Existing event extraction methods classify each argument role independently, ignoring the conceptual correlations between different argument roles.

Event Argument Extraction Event Extraction +1

Adversarial Training for Weakly Supervised Event Detection

1 code implementation NAACL 2019 Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, Peng Li

Modern weakly supervised methods for event detection (ED) avoid time-consuming human annotation and achieve promising results by learning from auto-labeled data.

Event Detection

Adversarial Multi-lingual Neural Relation Extraction

1 code implementation COLING 2018 Xiaozhi Wang, Xu Han, Yankai Lin, Zhiyuan Liu, Maosong Sun

To address these issues, we propose an adversarial multi-lingual neural relation extraction (AMNRE) model, which builds both consistent and individual representations for each sentence to consider the consistency and diversity among languages.

Question Answering Relation +2

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