SlowGuess/NAACL_2022_OCR huggingface dataset:
Shaden Shaar<sup>1</sup>, Firoj Alam<sup>1</sup>, Giovanni Da San Martino<sup>2</sup>, Preslav Nakov<sup>1</sup>
$^{2}$ University of Padova, Italy
{sshaar, falam, pnakov}@hbku.edu.qa
dasan@math.unipd.it
Recent years have seen the proliferation of disinformation and fake news online. Traditional approaches to mitigate these issues is to use manual or automatic fact-checking. Recently, another approach has emerged: checking whether the input claim has previously been fact-checked, which can be done automatically, and thus fast, while also offering credibility and explainability, thanks to the human fact-checking and explanations in the associated fact-checking article. Here, we focus on claims made in a political debate and we study the impact of modeling the context of the claim: both on the source side, i.e., in the debate, as well as on the target side, i.e., in the fact-checking explanation document. We do this by modeling the local context, the global context, as well as by means of co-reference resolution, and multihop reasoning over the sentences of the document describing the fact-checked claim. The experimental results show that each of these represents a valuable information source, but that modeling the source-side context is most important, and can yield  $10+$  points of absolute improvement over a state-of-the-art model.
The fight against dis/mis-information has become an urgent social and political matter. Online media have been widely used not only for social good, but also to mislead entire communities. Many fact-checking organizations, such as FactCheck.org, $^{1}$  Snopes, $^{2}$  PolitiFact, $^{3}$  and FullFact, $^{4}$  as well as some broader international initiatives such as the Credibility Coalition $^{5}$  and Eu-factcheck, $^{6}$  have emerged to address the problem (Stencel, 2019).
There have also been efforts to develop automatic systems to detect such content (Vo and Lee, 2018; Shu et al., 2017; Thorne and Vlachos, 2018; Li et al., 2016; Lazer et al., 2018; Vosoughi et al., 2018a; Nguyen et al., 2020), including the development of datasets (Augenstein et al., 2019), systems (Chernyavskiy et al., 2021b), and evaluation campaigns (Barrón-Cedeno et al., 2020; Nakov et al., 2021b,c; Shaar et al., 2021a; Nakov et al., 2022b).
An important issue with automatic systems is that journalists and fact-checkers often question their credibility for reasons such as (perceived) insufficient accuracy given the state of present technology, but also due to the lack of explanation about how the system has made its decision. On the other hand, manual fact-checking is time-consuming and does not scale. Yet, time is precious: it has been reported in the literature that fake news travels faster than real news (Vosoughi et al., 2018b), and that  $50\%$  of the spread of some very viral false claims has happened within the first ten minutes after they got published (Zaman et al., 2014). Such findings show the importance of real-time fake news detection, which can enable a timely intervention.
As both manual and automatic systems have their limitations, there have been proposals for human-in-the-loop settings, aiming to bring the best of both worlds. In order to enable such an approach, one question that arises is how to facilitate fact-checkers and journalists with automated systems (Nakov et al., 2021a). An immediate problem is to know whether a given input claim has been previously fact-checked by a reputable fact-checking organization. This would give the journalist a credible reference and could save her significant amount of time, as manually fact-checking a single nontrivial claim may take from 1-2 days to 1-2 weeks. While earlier studies have suggested that such a mechanism should be part of an end-to-end automated system, there has been limited work in this direction (Shaar et al., 2020a; Vo and Lee, 2020).
http://www.factcheck.org/
http://www.snopes.com/fact-check/
<sup>3</sup>http://www.politifact.com/
4http://fullfact.org/
<sup>5</sup>https://credibilitycoalition.org/
<https://eufactcheck.eu/
Findings of the Association for Computational Linguistics: NAACL 2022, pages 1619 - 1631
Figure 1: A pipeline of retrieving and ranking previously fact-checked claims.  $S_{i}$  is the claim (source),  $T^{t}$  is the title of the target,  $T_{j}$  is a sentence from the target.
At the time of COVID-19, there are a number of false claims and conspiracy theories spreading online, e.g., about Bill Gates and his chips in the COVID-19 vaccine, about garlic water as a cure, etc. Many such claims have already been debunked, but this does not stop them, as they keep being repeated, potentially in a slightly different form but with the same meaning. Thus, it is important to recognize such variations quickly, and possibly to post a reply in social media with a link to a fact-checking article. Similarly, in a scenario where a politician is being interviewed or is taking part in a debate, a quick check against a collection of previously fact-checked claims would make it possible to put him/her on the spot in real time.
However, the problem in a real-time scenario is that, unlike written text, interviews, debates, and speeches are more spontaneous, and the claims that are being made are often not clearly formulated in a single sentence. This is illustrated in Figure 1, where we can see a fragment from a Democratic debate as part of the 2016 US Presidential election, where Hillary Clinton said: "I waited until it had actually been negotiated because I did want to give the benefit of the doubt to the administration." Understanding this claim requires pronominal co-reference resolution (e.g., what does it refer to, is it CAFTA or is it TPP, as both are mentioned in the previous sentences?), more general co-reference (e.g., that the administration being discussed is the Obama administration), as well as general understanding of the conversation so far, and possibly general world knowledge about US politics at the time of the debate (e.g., that Hillary Clinton was Secretary of State when TPP was being discussed).
Moreover, previous research has shown that it is beneficial to match the input claim not only against the canonical verified claim that fact-checkers worked with, but against the entire article that they wrote explaining why the claim was ultimately judged to be true/false (Shaar et al., 2020a; Vo and Lee, 2020). This is because, in the fact-checking article, the claim is likely to be paraphrased in different ways, and there could also be background information and related terms, which can facilitate claim matching, and thus improve recall. This means that we need to make use of the global contextual information contained within the full text of the fact-checking article or at least the sentences next to the claim, i.e., the local context. Similarly, for the FEVER fact-checking task, which asks to fact-check against Wikipedia, it has been shown that multi-hop reasoning (Transformer-XH) over the sentences of the target article can help (Zhao et al., 2019), an observation that was further confirmed in the context of fact-checking political claims (Ostrowski et al., 2021). Transformer-XH uses a novel attention mechanism that naturally "hops" across the connected text sequences in addition to attending over tokens within each sequence. As claims and reasoning about them are manifested across documents, this hop-based attention mechanism constructs global contextualized representation to provide better joint multi-evidence reasoning. In the present work, we rely on Transformer-XH to extract and use global contextual information.
Based on the above considerations, we propose a framework that focuses on modeling coreference, local context (representation from neighboring sentences; see Section 4.2.2), and global context (representation from Transformer-XH; see Section 4.2.3), both on the source and on the target side, while also using multi-hop reasoning over the target side.
- We perform careful manual analysis to understand what makes detecting previously fact-checked claims a hard problem, and we categorize the claims by type. We release these annotations to enable further research.
- Unlike previous work, we focus on modeling the context both on the source side and on the target side, both locally and globally, using co-reference resolution and reasoning with Transformer-XH, which yields sizable improvements over state-of-the-art models of over ten MAP points absolute.
- We propose a realistic and challenging, time-sensitive and document-aware, data split compared to previous work, which we also release.<sup>7</sup>
The rest of the paper is organized as follows. Section 2 provides a brief overview of previous work. Section 3 introduces the dataset development process. Section 4 presents the experiments. Section 5 discusses the evaluation results. Finally, Section 6 concludes with lessons learned and points to possible directions for future work.
Below, we describe three relevant lines of research: on detecting previously fact-checked claims, on semantic matching and ranking, and on context modeling for factuality.
2.1 Previously Fact-Checked Claims
While there is a surge in research on automatic fact-checking, fully automatic systems suffer from credibility issues, e.g., in the eyes of journalists, and manual checking is still the norm. Thus, it is important to reduce that manual effort by detecting when a claim has already been fact-checked.
A recent survey has identified the task of detecting previously fact-checked claims as one of the most important ways in which automation can assist human fact-checkers (Nakov et al., 2021a). The task was recognized as an important element of the typical sequence of fact-checking steps (Vlachos and Riedel, 2014): (i) extracting statements that are to be fact-checked, (ii) constructing appropriate questions, (iii) obtaining the pieces of evidence from relevant sources, and (iv) reaching a verdict using that evidence. Hassan et al. (2017) also mentioned the task as an important component of their end-to-end fact-checking pipeline, but did not evaluate it as a component on its own right.
Recently, Shaar et al. (2020a) gave a formulation of the task of detecting previously fact-checked claims, and proposed a learning-to-rank approach combining BM25 retrieval with BERT-based semantic matching. They further developed two specialized datasets: (a) on political debates and speeches, using fact-checked claims from PolitiFact, and (b) on tweets, using claims from Snopes.
The CLEF 2020-2022 CheckThat! lab (Barrón-Cedeno et al., 2020; Hasanain et al., 2020; Shaar et al., 2020b; Nakov et al., 2021b,c; Shaar et al., 2021b; Nakov et al., 2022b,c,d) extended these datasets with additional data in English and Arabic, adding more data each year. The best systems (Pritzkau, 2021; Mihaylova et al., 2021; Chernyavskiy et al., 2021a) used a combination of BM25 retrieval, semantic similarity using sentence embeddings (Reimers and Gurevych, 2019), and reranking. Bouziane et al. (2020) further used external data from fact-checking datasets (Wang, 2017; Thorne et al., 2018; Wadden et al., 2020).
Chernyavskiy et al. (2022) fine-tuning BERT using batch-softmax contrastive loss as an alternative to mean squared error and triplet loss, and demonstrated sizable performance gains for a number of sentence scoring tasks, including detecting previously fact-checked claims.
Another recent work by Sheng et al. (2021) highlighted the importance of using lexical, semantic, and pattern-based information and proposed a re-ranker based on memory-enhanced transformers for claim matching.
Vo and Lee (2020) proposed a multi-modal text+image neural ranking model for detecting previously fact-checked claims about images.
However, none of the above work modeled the context of the input claim, which is our focus here.
$^{7}$ https://github.com/firojalam/Detecting-Previously-Fact-Checked-Claims.git
2.2 Semantic Matching and Ranking
Here, we focus on the textual formulation of the problem, as defined by Shaar et al. (2020a): given an input claim, we want to detect potentially matching previously fact-checked claims and to rank them accordingly. A related research area is semantic matching and ranking, as matching some InputClaim-VerClaim pairs might require sentence embeddings, natural language inference, and coreference resolution. An example of such a difficult pair is shown in Table 1, line 607. Recent relevant work has used neural approaches. Nie et al. (2019) proposed a semantic matching method that combines document retrieval, sentence selection, and claim verification neural models to extract claims and to verify them. Thorne et al. (2018) proposed a simple model, where pieces of evidence are concatenated together and then fed into a Natural Language Inference (NLI) model. Yoneda et al. (2018) used a four-stage approach that combines document and sentence retrieval with NLI. Hanselowski et al. (2018) used a BiLSTM-based enhanced sequential inference model (Chen et al., 2017) to rank candidate facts and to classify a new claim based on the selected facts. Several studies used model combination (i.e., document retrieval, sentence retrieval, and NLI to classify the retrieved sentences) with joint learning (Yoneda et al., 2018; Hidey and Diab, 2018; Luken et al., 2018).
2.3 Context Modeling for Factuality
Previous work has shown that modeling the context can help for predicting the check-worthiness of claims in political debates, e.g., the interaction between the debaters, and the reaction of the moderator and of the public to what was said (Gencheva et al., 2017; Atanasova et al., 2019b; Vasileva et al., 2019). The CLEF 2018-2022 CheckThat! lab had a shared task on this (Atanasova et al., 2018, 2019a; Shaar et al., 2020b, 2021c; Nakov et al., 2022a).
The CLEF-2018 CheckThat! lab featured a shared task on fact-checking a claim in the context of a political debate (Barrón-Cedeno et al., 2018; Nakov et al., 2018), and SemEval-2019 had a shared task on fact-checking in community question answering forums (Mihaylova et al., 2019).
Liu et al. (2020) proposed a kernel graph attention network to model evidence as a context for fact verification. Similarly, Zhou et al. (2019) used a fully connected evidence graph with multi-evidence information sources for fact verification.
Zhong et al. (2020) used different pre-trained Transformer models and a graph-based approach, i.e., graph convolutional network and graph attention network, for fact verification. Zhao et al. (2019) introduced extra hop attention to incorporate contextual information, while maintaining the Transformer capabilities, thus making it possible to learn a global representation of the different pieces of evidence and to jointly reason over the evidence graph. One of the limitations of their approach was the need for human-labeled evidence in relation to the input claims in existing fact-verification datasets. Ostrowski et al. (2021) developing a dataset of annotated pieces of evidence associated with input claims and used multihop attention to make a prediction about the factuality of a claim.
Unlike the above work, here we target a different task: detecting previously fact-checked claims as opposed to check-worthiness prediction or fact-checking a claim. Moreover, while the above work was limited to the target context, here we also model the source context, which turns out to be much more important.
Here, we focus on the problem of detecting previously fact-checked claims, using the task formulation and an adaptation of data from (Shaar et al., 2020a). They had two datasets: one on matching tweets against Snopes claims, and another one on matching claims in the context of a political debate to PolitiFact claims. Here, we focus on the latter, and we perform a close analysis of the claims and what makes them easy/hard to match.
We experimented with their PolitiFact dataset, which targets claims related to US politics. After a US political debate, speech, or interview, fact-checking journalists from PolitiFact would select few claims made in the event and would verify them either from scratch or by linking them to a previously fact-checked claim. Each fact-checked claim has an associated article stating its degree of factuality along with an explanation of how the fact-checkers arrived at their verdict. The dataset has two parts: (i) verified claims {normalized VerClaim, article title, and article text}, (ii) transcripts of the political events (e.g., debates). Shaar et al. (2020a) annotated the data by linking sentences from the transcript (InputClaim) to one or more verified claims (out of 16,636 claims in PolitiFact).
github.com/sshaar/That-is-a-Known-Lie
Line No.	Type	Input Claim	Verified Claim
255	clean	D. Trump:	Hillary Clinton wanted the wall.	Says Hillary Clinton “wanted the wall.”
695	part-of	C. Wallas:	And since then, as we all know, nine women have come forward and have said that you either groped them or kissed them without their consent.	The stories from women saying he groped or forced himself on them “largely have been debunked.”
699	part-of	D. Trump:	Well, first of all, those stories have been largely debunked.	The stories from women saying he groped or forced himself on them “largely have been debunked.”
688	clean-hard	D. Trump:	She gave us ISIS as sure as you are sitting there.	Hillary Clinton invented ISIS with her stupid policies. She is responsible for ISIS.
605	D. Trump:	Now she wants to sign *TransPacific Partnership.
607	context-dep	D. Trump:	She lied when she said she didn’t call it the gold standard in one of the debates.	Says Hillary Clinton called the TransPacific Partnership “the gold standard. You called it the gold standard of trade deals. You said its the finest deal you’ve ever seen.”
Table 1: Fragment from the 3rd US Presidential debate in 2016 showing the verified claims chosen by PolitiFact and the fine-grained category of the pair. Most input sentences have no verified claim, e.g., see line 605.
To further analyze the dataset, we looked at the InputClaim-VerClaim pairs, and we manually categorized them into one of the following categories:
1. clean : A clean pair is a self-contained InputClaim with a VerClaim that directly verifies it (see line 255 in Table 1 as an example).
2. clean-hard: A clean-hard pair is a self-contained InputClaim with a VerClaim that indirectly verifies it (see line 688 in Table 1).
3. part-of: A part-of pair's InputClaim is not self-contained and requires the addition of other sentences from the transcript to fully form a single claim.
4. context-dep: A context-dep pair is similar to clean and clean-hard, but the InputClaim is not self-contained and needs co-reference.
The above categories include all types of pairs we have seen. Moreover, since the dataset is constructed from speeches, debates, and interviews, the structure of the InputClaim-VerClaim pairs differs. For example, in debates, we see more part-of examples, as there are multiple question-answer claim pairs, as well as back-and-forth arguments splitting the claims into multiple sentences.
The annotations were performed by three annotators who are experts in fact-checking (and co-authors of this paper), using the above definitions for the categories. We consolidated their annotations using majority voting, and they had a consolidation discussion for cases with no majority. The Fleiss Kappa inter-annotator agreement was 0.5, which corresponds to moderate agreement, which is reasonable for such a complex annotation task.
Table 1 shows examples of InputClaim-VerClaim pairs that illustrate the four categories. We can see that the task goes beyond simple textual similarity and natural language inference, as the examples in lines 607 and 695-699 show. Moreover, matching context-dep pairs (lines 605-607) requires understanding the InputClaim's local context, while matching clean-hard pairs (line 688) requires analysis of the overall global context of the VerClaim.
Finally, we should note while annotating the data into the above four categories, we found out that a small number of InputClaim-VerClaim pairs in (Shaar et al., 2020a) were false matches (which happened, as they did the matching automatically, without manually double-checking every single example). We removed these pairs, and thus our reported number of pairs is slightly lower than theirs.
InputClaim-VerClaim pairs	695
- clean	291	42%
- clean-hard	210	30%
-part-of	68	10%
-context-dep	126	18%
Total # of verified claims (to match against)	16,636
Table 2 shows statistics about the distribution of the four categories of claims in our dataset. We can see that clean and clean-hard are the most frequent categories, while part-of is the least frequent one.
We further observed that Shaar et al. (2020a) dealt with each InputClaim independently, i.e., at the sentence level. This is problematic because for part-of claims we could end up splitting them and putting them in different sets: one in training, and one in testing. Moreover, splitting the dataset in this way means that the examples for a given topic can split between training and test, and thus information can leak, e.g., a claim can be repeated. Therefore, we considered new splits for the data:
- Debate-Level Chrono: We split the data chronologically. We use the first 50 debates for training, and the last 20 for testing. Specifically, we have 554 pairs for training, and 141 pairs for testing. This is a more realistic scenario, where we would only have access to earlier debates, and we can use them to make decisions about claims made in future debates. The complexity of this setting is also reflected in the MAP score as shown in Table 3. We see that this score is lower than the best model in previous work (last row). This is because this setting is complex as we use a model trained on debates and speeches from 2012-2018, and we test on debates from 2019. Across those different time frames, different politicians discuss different topics.
- Debate-Level Semi-Chrono: We split the data per year, e.g., for year 2018, we divide the transcripts into train and test with 80/20 splits, and then we train and evaluate using the same reranking model. In Table 3, we can see an improvement with this setting compared to the Debate-Level Chrono setting. This might be because the same politicians discuss the same/similar issues throughout the same year.
Table 2: Statistics about our dataset: total number of InputClaim-VerClaim pairs and of VerClaims in PolitiFact to match an InputClaim against.
Split	MAP
Debate-Level – Chrono	0.429
Debate-Level – Semi-chrono	0.539
Debate-Level – Random	0.590
Sentence-Level – Random (Shaar et al., 2020a)	0.602
Table 3: MAP scores of the reranker models when using four different splits representing different scenarios. We use Debate-Level - Chrono for our experiments.
- Debate-Level Random: We randomly choose  $80\%$  of the debates for training and the remaining ones for testing. This is a comparatively easier setting as the data is randomly distributed in training and testing. This is also reflected in the results in Table 3. The reason could be that politicians repeat themselves a lot, especially in two consecutive political events, and the random split can lead to having two similar debates/speeches in two splits.
- Sentence Level Random: This is the setting in (Shaar et al., 2020a), where sentences from the debates are randomly divided into train and test in a proportion of 80:20. This is the most unrealistic split.
In our experiments, we chose to use the most realistic, but also the hardest setup: Debate-Level Chrono. As a result, our MAP score, when experimenting with the state-of-the-art model of (Shaar et al., 2020a), decreases from 0.602 to 0.429.
Below, we first introduce the experimental setup for our baseline, and then we describe our proposed model that takes the context of the input claim into account, both on the source and on the target side.
From our analysis of the dataset (described in Section 3), we conclude that  $(i)$  we need to resolve the references in the InputClaim,  $(ii)$  to capture the local context of the InputClaim, and  $(iii)$  to encapsulate the global context of the VerClaim.