748 research outputs found
Automated Fact Checking in the News Room
Fact checking is an essential task in journalism; its importance has been
highlighted due to recently increased concerns and efforts in combating
misinformation. In this paper, we present an automated fact-checking platform
which given a claim, it retrieves relevant textual evidence from a document
collection, predicts whether each piece of evidence supports or refutes the
claim, and returns a final verdict. We describe the architecture of the system
and the user interface, focusing on the choices made to improve its
user-friendliness and transparency. We conduct a user study of the
fact-checking platform in a journalistic setting: we integrated it with a
collection of news articles and provide an evaluation of the platform using
feedback from journalists in their workflow. We found that the predictions of
our platform were correct 58\% of the time, and 59\% of the returned evidence
was relevant
Challenges and Opportunities for Journalistic Knowledge Platforms
Journalism is under pressure from loss of advertisement and revenues, while experiencing an increase in digital consumption and user demands for quality journalism and trusted sources. Journalistic Knowledge Platforms (JKPs) are an emerging generation of platforms which combine state-of-the-art artificial intelligence (AI) techniques such as knowledge graphs, linked open data (LOD), and natural-language processing (NLP) for transforming newsrooms and leveraging information technologies to increase the quality and lower the cost of news production. In order to drive research and design better JKPs that allow journalists to get most benefits out of them, we need to understand what challenges and opportunities JKPs are facing. This paper presents an overview of the main challenges and opportunities involved in JKPs which have been manually extracted from literature with the support of natural language processing and understanding techniques. These challenges and opportunities are organised in: stakeholders, information, functionalities, components, techniques and other aspects.publishedVersio
KILT: a Benchmark for Knowledge Intensive Language Tasks
Challenging problems such as open-domain question answering, fact checking,
slot filling and entity linking require access to large, external knowledge
sources. While some models do well on individual tasks, developing general
models is difficult as each task might require computationally expensive
indexing of custom knowledge sources, in addition to dedicated infrastructure.
To catalyze research on models that condition on specific information in large
textual resources, we present a benchmark for knowledge-intensive language
tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia,
reducing engineering turnaround through the re-use of components, as well as
accelerating research into task-agnostic memory architectures. We test both
task-specific and general baselines, evaluating downstream performance in
addition to the ability of the models to provide provenance. We find that a
shared dense vector index coupled with a seq2seq model is a strong baseline,
outperforming more tailor-made approaches for fact checking, open-domain
question answering and dialogue, and yielding competitive results on entity
linking and slot filling, by generating disambiguated text. KILT data and code
are available at https://github.com/facebookresearch/KILT.Comment: accepted at NAACL 202
Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction
In recent years there is a surge of interest in applying distant supervision
(DS) to automatically generate training data for relation extraction (RE). In
this paper, we study the problem what limits the performance of DS-trained
neural models, conduct thorough analyses, and identify a factor that can
influence the performance greatly, shifted label distribution. Specifically, we
found this problem commonly exists in real-world DS datasets, and without
special handing, typical DS-RE models cannot automatically adapt to this shift,
thus achieving deteriorated performance. To further validate our intuition, we
develop a simple yet effective adaptation method for DS-trained models, bias
adjustment, which updates models learned over the source domain (i.e., DS
training set) with a label distribution estimated on the target domain (i.e.,
test set). Experiments demonstrate that bias adjustment achieves consistent
performance gains on DS-trained models, especially on neural models, with an up
to 23% relative F1 improvement, which verifies our assumptions. Our code and
data can be found at
\url{https://github.com/INK-USC/shifted-label-distribution}.Comment: 13 pages: 10 pages paper, 3 pages appendix. Appears at EMNLP 201
Supporting Newsrooms with Journalistic Knowledge Graph Platforms: Current State and Future Directions
Increasing competition and loss of revenues force newsrooms to explore new digital solutions. The new solutions employ artificial intelligence and big data techniques such as machine learning and knowledge graphs to manage and support the knowledge work needed in all stages of news production. The result is an emerging type of intelligent information system we have called the Journalistic Knowledge Platform (JKP). In this paper, we analyse for the first time knowledge graph-based JKPs in research and practice. We focus on their current state, challenges, opportunities and future directions. Our analysis is based on 14 platforms reported in research carried out in collaboration with news organisations and industry partners and our experiences with developing knowledge graph-based JKPs along with an industry partner. We found that: (a) the most central contribution of JKPs so far is to automate metadata annotation and monitoring tasks; (b) they also increasingly contribute to improving background information and content analysis, speeding-up newsroom workflows and providing newsworthy insights; (c) future JKPs need better mechanisms to extract information from textual and multimedia news items; (d) JKPs can provide a digitalisation path towards reduced production costs and improved information quality while adapting the current workflows of newsrooms to new forms of journalism and readers’ demands.publishedVersio
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