Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)最新文献

Veritas Annotator: Discovering the Origin of a Rumour 真理注释者:发现谣言的起源

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 2019-11-03 DOI: 10.18653/v1/D19-6614

Lucas Azevedo, Mohamed Moustafa

引用次数: 1

Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism 基于多头注意机制的双指针网络多实体关系提取

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 2019-11-01 DOI: 10.18653/v1/D19-6608

Seongsik Park, H. Kim

引用次数: 1

Team GPLSI. Approach for automated fact checking 团队GPLSI。自动事实检查的方法

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 2019-11-01 DOI: 10.18653/v1/D19-6617

Aimée Alonso-Reina, Robiert Sepúlveda-Torres, E. Saquete, M. Palomar

引用次数: 13

Interactive Evidence Detection: train state-of-the-art model out-of-domain or simple model interactively? 交互式证据检测:交互式地训练最先进的域外模型还是简单模型?

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 2019-11-01 DOI: 10.18653/v1/D19-6613

C. Stahlhut

{"title":"Interactive Evidence Detection: train state-of-the-art model out-of-domain or simple model interactively?","authors":"C. Stahlhut","doi":"10.18653/v1/D19-6613","DOIUrl":"https://doi.org/10.18653/v1/D19-6613","url":null,"abstract":"Finding evidence is of vital importance in research as well as fact checking and an evidence detection method would be useful in speeding up this process. However, when addressing a new topic there is no training data and there are two approaches to get started. One could use large amounts of out-of-domain data to train a state-of-the-art method, or to use the small data that a person creates while working on the topic. In this paper, we address this problem in two steps. First, by simulating users who read source documents and label sentences they can use as evidence, thereby creating small amounts of training data for an interactively trained evidence detection model; and second, by comparing such an interactively trained model against a pre-trained model that has been trained on large out-of-domain data. We found that an interactively trained model not only often out-performs a state-of-the-art model but also requires significantly lower amounts of computational resources. Therefore, especially when computational resources are scarce, e.g. no GPU available, training a smaller model on the fly is preferable to training a well generalising but resource hungry out-of-domain model.","PeriodicalId":153447,"journal":{"name":"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133564047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Neural Multi-Task Learning for Stance Prediction 姿态预测的神经多任务学习

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 2019-11-01 DOI: 10.18653/v1/D19-6603

Wei Fang, Moin Nadeem, Mitra Mohtarami, James R. Glass

引用次数: 16

Hybrid Models for Aspects Extraction without Labelled Dataset 无标记数据集的方面提取混合模型

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 2019-11-01 DOI: 10.18653/v1/D19-6611

W. Khong, Lay-Ki Soon, Hui-Ngo Goh

{"title":"Hybrid Models for Aspects Extraction without Labelled Dataset","authors":"W. Khong, Lay-Ki Soon, Hui-Ngo Goh","doi":"10.18653/v1/D19-6611","DOIUrl":"https://doi.org/10.18653/v1/D19-6611","url":null,"abstract":"One of the important tasks in opinion mining is to extract aspects of the opinion target. Aspects are features or characteristics of the opinion target that are being reviewed, which can be categorised into explicit and implicit aspects. Extracting aspects from opinions is essential in order to ensure accurate information about certain attributes of an opinion target is retrieved. For instance, a professional camera receives a positive feedback in terms of its functionalities in a review, but its overly high price receives negative feedback. Most of the existing solutions focus on explicit aspects. However, sentences in reviews normally do not state the aspects explicitly. In this research, two hybrid models are proposed to identify and extract both explicit and implicit aspects, namely TDM-DC and TDM-TED. The proposed models combine topic modelling and dictionary-based approach. The models are unsupervised as they do not require any labelled dataset. The experimental results show that TDM-DC achieves F1-measure of 58.70%, where it outperforms both the baseline topic model and dictionary-based approach. In comparison to other existing unsupervised techniques, the proposed models are able to achieve higher F1-measure by approximately 3%. Although the supervised techniques perform slightly better, the proposed models are domain-independent, and hence more versatile.","PeriodicalId":153447,"journal":{"name":"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)","volume":"30 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120885272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The FEVER2.0 Shared Task 共享任务

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 1900-01-01 DOI: 10.18653/v1/D19-6601

James Thorne, Andreas Vlachos, O. Cocarascu, Christos Christodoulopoulos, Arpit Mittal

{"title":"The FEVER2.0 Shared Task","authors":"James Thorne, Andreas Vlachos, O. Cocarascu, Christos Christodoulopoulos, Arpit Mittal","doi":"10.18653/v1/D19-6601","DOIUrl":"https://doi.org/10.18653/v1/D19-6601","url":null,"abstract":"We present the results of the second Fact Extraction and VERification (FEVER2.0) Shared Task. The task challenged participants to both build systems to verify factoid claims using evidence retrieved from Wikipedia and to generate adversarial attacks against other participant’s systems. The shared task had three phases: building, breaking and fixing. There were 8 systems in the builder’s round, three of which were new qualifying submissions for this shared task, and 5 adversaries generated instances designed to induce classification errors and one builder submitted a fixed system which had higher FEVER score and resilience than their first submission. All but one newly submitted systems attained FEVER scores higher than the best performing system from the first shared task and under adversarial evaluation, all systems exhibited losses in FEVER score. There was a great variety in adversarial attack types as well as the techniques used to generate the attacks, In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.","PeriodicalId":153447,"journal":{"name":"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129605811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 70

Scalable Knowledge Graph Construction from Text Collections 从文本集合构建可扩展的知识图谱

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 1900-01-01 DOI: 10.18653/v1/D19-6607

R. Clancy, I. Ilyas, Jimmy J. Lin

引用次数: 12

FEVER Breaker’s Run of Team NbAuzDrLqg 狂热破坏者队NbAuzDrLqg的运行

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 1900-01-01 DOI: 10.18653/v1/D19-6615

Youngwoo Kim, J. Allan

引用次数: 3

Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims? 事实检验或心理语言学:如何分辨真假?

Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 1900-01-01 DOI: 10.18653/v1/D19-6602

A. Wawer, Grzegorz Wojdyga, Justyna Sarzyńska-Wawer

引用次数: 2