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Editors contains: "Nakov, Preslav"

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  1. ; ; ; (, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))
    Muresan, Smaranda; Nakov, Preslav; Villavicencio, Aline (Ed.)
    Phonemes are defined by their relationship to words: changing a phoneme changes the word. Learning a phoneme inventory with little supervision has been a longstanding challenge with important applications to under-resourced speech technology. In this paper, we bridge the gap between the linguistic and statistical definition of phonemes and propose a novel neural discrete representation learning model for self-supervised learning of phoneme inventory with raw speech and word labels. Under mild assumptions, we prove that the phoneme inventory learned by our approach converges to the true one with an exponentially low error rate. Moreover, in experiments on TIMIT and Mboshi benchmarks, our approach consistently learns a better phoneme-level representation and achieves a lower error rate in a zero-resource phoneme recognition task than previous state-of-the-art self-supervised representation learning algorithms. 
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    Full Text Available 
  2. ; (, Proceedings of the 2020 Truth and Trust Online Conference (TTO 2020))
    De Cristofaro, Emiliano; Nakov, Preslav (Ed.)
    Google’s reviewed claims feature was an early attempt to incorporate additional credibility signals from fact-checking onto the search results page. The feature, which appeared when users searched for the name of a subset of news publishers, was criticized by dozens of publishers for its errors and alleged anticonservative bias. By conducting an audit of news publisher search results and focusing on the critiques of publishers, we find that there is a lack of consensus between fact-checking ecosystem stakeholders that may be important to address in future iterations of public facing fact-checking tools. In particular, we find that a lack of transparency coupled with a lack of consensus on what makes a fact-check relevant to a news article led to the breakdown of reviewed claims. 
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    Accepted Manuscript Full Text Available
  3. ; ; (, Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda)
    Feldman, Anna; Da San Martino, Giovanni; Leberknight, Chris; Nakov, Preslav (Ed.)
    The explosion of online health news articles runs the risk of the proliferation of low-quality information. Within the existing work on fact-checking, however, relatively little attention has been paid to medical news. We present a health news classification task to determine whether medical news articles satisfy a set of review criteria deemed important by medical experts and health care journalists. We present a dataset of 1,119 health news paired with systematic reviews. The review criteria consist of six elements that are essential to the accuracy of medical news. We then present experiments comparing the classical token-based approach with the more recent transformer-based models. Our results show that detecting qualitative lapses is a challenging task with direct ramifications in misinformation, but is an important direction to pursue beyond assigning True or False labels to short claims. 
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    Accepted Manuscript Full Text Available