Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer 2021
DOI: 10.18653/v1/2021.acl-long.122
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Factuality Assessment as Modal Dependency Parsing

Abstract: As the sources of information that we consume everyday rapidly diversify, it is becoming increasingly important to develop NLP tools that help to evaluate the credibility of the information we receive. A critical step towards this goal is to determine the factuality of events in text. In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting… Show more

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Cited by 7 publications
(14 citation statements)
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“…We present Excavator, a machine reading system that automatically constructs a Temporal and Causal Analysis Graph for COVID-19 by reading open-source text documents such as news and scientific publications. Our next steps are to integrate Modal Dependency Parsing (Yao et al, 2021) for event factuality assessment, and cross-lingual transfer learning (Nguyen et al, 2021) to make Excavator applicable to more languages.…”
Section: Discussionmentioning
confidence: 99%
“…We present Excavator, a machine reading system that automatically constructs a Temporal and Causal Analysis Graph for COVID-19 by reading open-source text documents such as news and scientific publications. Our next steps are to integrate Modal Dependency Parsing (Yao et al, 2021) for event factuality assessment, and cross-lingual transfer learning (Nguyen et al, 2021) to make Excavator applicable to more languages.…”
Section: Discussionmentioning
confidence: 99%
“…Specifically, for each candidate mention span, the algorithm computes a distribution over possible antecedent spans from the mention score (whether it is likely to be a mention) and the compatibility score of the two spans, which itself involves a feed-forward network to compute. Two more structural parsing examples in this vein are temporal dependency parsing (Ross et al, 2020) and modal dependency parsing (Yao et al, 2021). These studies approach tree building algorithmically by first performing a classification problem to identify suitable dependency pairs, then ranking them to construct a valid tree.…”
Section: Fine-tuning the Plm In Customized Modelsmentioning
confidence: 99%
“…For instance, in Figure 1, a person is an entity but is not a conceiver as it is not the source of any event. As a result, Yao et al (2021) report relatively low conceiver extraction F-score compared to event extraction (70.4% for conceiver extraction vs. 90.8% for event extraction). Errors in conceiver extraction will propagate to the structure building stage, leading to lower overall MDS parsing accuracy.…”
Section: Introductionmentioning
confidence: 96%
“…For example, in Figure 1, our judgment of whether the event travelled has happened also crucially depends on the credibility of the information source, Jeroen Weimar, in addition to the level of certainty the information source holds towards the event. Yao et al (2021) develop the first modal dependency parser by first separately extracting events and conceivers, then building up the MDS bottomup with a ranking model. One shortcoming of this approach is that it fails to capture the fact that the status of an entity as a conceiver is conditioned on its being the information source of an event.…”
Section: Introductionmentioning
confidence: 99%
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