221 research outputs found
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!
Argumentation mining (AM) requires the identification of complex discourse
structures and has lately been applied with success monolingually. In this
work, we show that the existing resources are, however, not adequate for
assessing cross-lingual AM, due to their heterogeneity or lack of complexity.
We therefore create suitable parallel corpora by (human and machine)
translating a popular AM dataset consisting of persuasive student essays into
German, French, Spanish, and Chinese. We then compare (i) annotation projection
and (ii) bilingual word embeddings based direct transfer strategies for
cross-lingual AM, finding that the former performs considerably better and
almost eliminates the loss from cross-lingual transfer. Moreover, we find that
annotation projection works equally well when using either costly human or
cheap machine translations. Our code and data are available at
\url{http://github.com/UKPLab/coling2018-xling_argument_mining}.Comment: Accepted at Coling 201
NLP-Based Techniques for Cyber Threat Intelligence
In the digital era, threat actors employ sophisticated techniques for which,
often, digital traces in the form of textual data are available. Cyber Threat
Intelligence~(CTI) is related to all the solutions inherent to data collection,
processing, and analysis useful to understand a threat actor's targets and
attack behavior. Currently, CTI is assuming an always more crucial role in
identifying and mitigating threats and enabling proactive defense strategies.
In this context, NLP, an artificial intelligence branch, has emerged as a
powerful tool for enhancing threat intelligence capabilities. This survey paper
provides a comprehensive overview of NLP-based techniques applied in the
context of threat intelligence. It begins by describing the foundational
definitions and principles of CTI as a major tool for safeguarding digital
assets. It then undertakes a thorough examination of NLP-based techniques for
CTI data crawling from Web sources, CTI data analysis, Relation Extraction from
cybersecurity data, CTI sharing and collaboration, and security threats of CTI.
Finally, the challenges and limitations of NLP in threat intelligence are
exhaustively examined, including data quality issues and ethical
considerations. This survey draws a complete framework and serves as a valuable
resource for security professionals and researchers seeking to understand the
state-of-the-art NLP-based threat intelligence techniques and their potential
impact on cybersecurity
Validating Multimedia Content Moderation Software via Semantic Fusion
The exponential growth of social media platforms, such as Facebook and
TikTok, has revolutionized communication and content publication in human
society. Users on these platforms can publish multimedia content that delivers
information via the combination of text, audio, images, and video. Meanwhile,
the multimedia content release facility has been increasingly exploited to
propagate toxic content, such as hate speech, malicious advertisements, and
pornography. To this end, content moderation software has been widely deployed
on these platforms to detect and blocks toxic content. However, due to the
complexity of content moderation models and the difficulty of understanding
information across multiple modalities, existing content moderation software
can fail to detect toxic content, which often leads to extremely negative
impacts.
We introduce Semantic Fusion, a general, effective methodology for validating
multimedia content moderation software. Our key idea is to fuse two or more
existing single-modal inputs (e.g., a textual sentence and an image) into a new
input that combines the semantics of its ancestors in a novel manner and has
toxic nature by construction. This fused input is then used for validating
multimedia content moderation software. We realized Semantic Fusion as DUO, a
practical content moderation software testing tool. In our evaluation, we
employ DUO to test five commercial content moderation software and two
state-of-the-art models against three kinds of toxic content. The results show
that DUO achieves up to 100% error finding rate (EFR) when testing moderation
software. In addition, we leverage the test cases generated by DUO to retrain
the two models we explored, which largely improves model robustness while
maintaining the accuracy on the original test set.Comment: Accepted by ISSTA 202
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