which architectures did they experiment with?
For stance detection they used MLP, for evidence extraction they used Tf-idf and BiLSTM, for claim validation they used MLP, BiLSTM and SVM
Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web. In order to achieve satisfactory performance, machine learning methods require a large corpus with reliable annotations for the different tasks in the fact-checking process. Having analyzed existing fact-checking corpora, we found that none of them meets these criteria in full. They are either too small in size, do not provide detailed annotations, or are limited to a single domain. Motivated by this gap, we present a new substantially sized mixed-domain corpus with annotations of good quality for the core fact-checking tasks: document retrieval, evidence extraction, stance detection, and claim validation. To aid future corpus construction, we describe our methodology for corpus creation and annotation, and demonstrate that it results in substantial inter-annotator agreement. As baselines for future research, we perform experiments on our corpus with a number of model architectures that reach high performance in similar problem settings. Finally, to support the development of future models, we provide a detailed error analysis for each of the tasks. Our results show that the realistic, multi-domain setting defined by our data poses new challenges for the existing models, providing opportunities for considerable improvement by future systems.
The ever-increasing role of the Internet as a primary communication channel is arguably the single most important development in the media over the past decades. While it has led to unprecedented growth in information coverage and distribution speed, it comes at a cost. False information can be shared through this channel reaching a much wider audience than traditional means of disinformation BIBREF0. While human fact-checking still remains the primary method to counter this issue, the amount and the speed at which new information is spread makes manual validation challenging and costly. This motivates the development of automated fact-checking pipelines BIBREF1, BIBREF2, BIBREF3 consisting of several consecutive tasks. The following four tasks are commonly included in the pipeline. Given a controversial claim, document retrieval is applied to identify documents that contain important information for the validation of the claim. Evidence extraction aims at retrieving text snippets or sentences from the identified documents that are related to the claim. This evidence can be further processed via stance detection to infer whether it supports or refutes the claim. Finally, claim validation assesses the validity of the claim given the evidence. Automated fact-checking has received significant attention in the NLP community in the past years. Multiple corpora have been created to assist the development of fact-checking models, varying in quality, size, domain, and range of annotated phenomena. Importantly, the successful development of a full-fledged fact-checking system requires that the underlying corpus satisfies certain characteristics. First, training data needs to contain a large number of instances with high-quality annotations for the different fact-checking sub-tasks. Second, the training data should not be limited to a particular domain, since potentially wrong information sources can range from official statements to blog and Twitter posts. We analyzed existing corpora regarding their adherence to the above criteria and identified several drawbacks. The corpora introduced by BIBREF4, BIBREF5, BIBREF6 are valuable for the analysis of the fact-checking problem and provide annotations for stance detection. However, they contain only several hundreds of validated claims and it is therefore unlikely that deep learning models can generalize to unobserved claims if trained on these datasets. A corpus with significantly more validated claims was introduced by BIBREF2. Nevertheless, for each claim, the corpus provides 30 documents which are retrieved from the web using the Google search engine instead of a document collection aggregated by fact-checkers. Thus, many of the documents are unrelated to the claim and important information for the validation may be missing. The FEVER corpus constructed by BIBREF1 is the largest corpus available for the development of automated fact-checking systems. It consists of 185,445 validated claims with annotated documents and evidence for each of them. The corpus therefore allows training deep neural networks for automated fact-checking, which reach higher performance than shallow machine learning techniques. However, the corpus is based on synthetic claims derived from Wikipedia sentences rather than natural claims that originate from heterogeneous web sources. In order to address the drawbacks of existing datasets, we introduce a new corpus based on the Snopes fact-checking website. Our corpus consists of 6,422 validated claims with comprehensive annotations based on the data collected by Snopes fact-checkers and our crowd-workers. The corpus covers multiple domains, including discussion blogs, news, and social media, which are often found responsible for the creation and distribution of unreliable information. In addition to validated claims, the corpus comprises over 14k documents annotated with evidence on two granularity levels and with the stance of the evidence with respect to the claims. Our data allows training machine learning models for the four steps of the automated fact-checking process described above: document retrieval, evidence extraction, stance detection, and claim validation. The contributions of our work are as follows: 1) We provide a substantially sized mixed-domain corpus of natural claims with annotations for different fact-checking tasks. We publish a web crawler that reconstructs our dataset including all annotations. For research purposes, we are allowed to share the original corpus. 2) To support the creation of further fact-checking corpora, we present our methodology for data collection and annotation, which allows for the efficient construction of large-scale corpora with a substantial inter-annotator agreement. 3) For evidence extraction, stance detection, and claim validation we evaluate the performance of high-scoring systems from the FEVER shared task BIBREF7 and the Fake News Challenge BIBREF8 as well as the Bidirectional Transformer model BERT BIBREF9 on our data. To facilitate the development of future fact-checking systems, we release the code of our experiments. 4) Finally, we conduct a detailed error analysis of the systems trained and evaluated on our data, identifying challenging fact-checking instances which need to be addressed in future research.