Implicit Temporal Reasoning for Evidence-Based Fact-Checking (2023)

Abstract: Leveraging contextual knowledge has become standard practice in automated claim verification, yet the impact of temporal reasoning has been largely overlooked. Our study demonstrates that time positively influences the claim verification process of evidence-based fact-checking. The temporal aspects and relations between claims and evidence are first established through grounding on shared timelines, which are constructed using publication dates and time expressions extracted from their text. Temporal information is then provided to RNN-based and Transformer-based classifiers before or after claim and evidence encoding. Our time-aware fact-checking models surpass base models by up to 9% Micro F1 (64.17%) and 15% Macro F1 (47.43%) on the MultiFC dataset. They also outperform prior methods that explicitly model temporal relations between evidence. Our findings show that the presence of temporal information and the manner in which timelines are constructed greatly influence how fact-checking models determine the relevance and supporting or refuting character of evidence documents.

## introduction automatically verifying information and flagging engineered falsities have been high on the political, media, and -subsequently -research agenda for quite some time @xcite . however, the role of time in machine-assisted factchecking has been inadequately investigated. time can affect the veracity of previously uttered claims and the relevance of supporting or refuting evidence. this is evident in research, for example, where newly acquired knowledge may question, confirm, or refute established facts. this study proposes to ground claims and associated evidence in in this study, we let the model implicitly reason over the temporal aspects of the claim and evidence, and their relations. for this, both inputs are grounded at two levels on a shared timeline: at the document level using their publication dates (in yellow, dotted line) and at the content-level using time expressions in their text (in blue, dashed line). grounding a claim or evidence document in time is a complex task. on the one hand, it can be achieved through document-level grounding, which involves positioning the entire document on a timeline based on its publication date. on the other hand, a document may discuss several events that have occurred in the past, present, or future. to fa-cilitate more fine-grained grounding on the content level, time expressions in the text are used to to place the document on multiple positions on a timeline. such expressions can be explicit (e.g., 27 june 2022), implicit (e.g., christmas 2022), and relative (e.g., mid-september), which may require additional temporal information for grounding @xcite . in this study, we ground claims and evidence on both the document and content level. this is accomplished by extracting and normalising their publication date and in-text time expressions, and subsequently relating them in terms of distance in time. this enables fact-checking models to reason over the temporal relations between a claim and its evidence on more than one level. contributions this study demonstrates that reasoning over temporal aspects and relations of claims and evidence not only improves factchecking models' prediction performance but also influences their estimation of the relevance and the supporting/refuting character of the evidence. the effects on performance are even reinforced when claims and evidence are grounded at both the document and content level, showing the appropriateness of multi-level temporal reasoning in automated fact-checking. ## related work automated fact-checking is usually a two-phase process consisting of claim detection/selection and claim verification @xcite . time is arguably important in both phases. when detecting and ultimately selecting claims to fact-check, fact-checkers heed the current interest of the public in certain topics and election cycles, and rank the claims accordingly @xcite . moreover, many selected claims mention dates or time periods @xcite . @xcite looked in the past and filtered out claims that are semantically similar to previously fact-checked claims to expedite the claim selection process. while evidence-based claim verification has been widely studied @xcite @xcite @xcite @xcite , few studies explicitly focused on incorporating temporal reasoning in the verification process. zhou et al. (2020) constructed (entity, value, time)-tuples representing supposedly temporal facts and verified their correctness using probabilistic graphical models. @xcite constrained the evidence ranking in fact-checking models on time using silver-standard evidence rankings respecting four assumptions on temporal relevance. instead of verifying the temporal correctness of claim tuples or explicitly enforcing time-dependent evidence rankings, we let fact-checking models reason implicitly over temporal aspects of claims and evidence in natural language when checking the claims. ## task description classifier f takes a textual claim c and an associated set of n text documents {e i } n serving as evidence of c, and returns a claim veracity label y. to allow f to reason over temporal aspects of c and e i , we extract and normalise publication dates and time expressions in c and e i , and assign them to time buckets. temporal representations c t and e i,t are sequences of time bucket indices and are given as additional input to f : to obtain temporal representations c t and e i,t , we ground c and e i in time by positioning them on a joint timeline using either their publication date (c @xmath0; e i,@xmath1,t ) or in-text time expressions (c @xmath2; e i,@xmath3,t ## discussion weighting text and time we ran experiments with various weight values (α, β, γ) for combining the text features of a claim and its evidence with their temporal information foot_3 . ## conclusion grounding claims and associated evidence documents on a shared timeline and implicitly reasoning over their temporal relations noticeably improves the verification performance of automated fact-checking models. time plays a dual role in this process, serving both as a source of information for verifying claims, as well as influencing the evaluation of the relevance and supporting or refuting nature of evidence documents. further research may look into integrating temporal reasoning in claim detection and evidence retrieval processes or implementing even more sophisticated temporal reasoning during claim verification by examining the temporality of events discussed in a claim and their relation to the evidence.