SlowGuess/NAACL_2021_OCR huggingface dataset:
Tal Schuster Adam Fisch Regina Barzilay
{tals, fisch, regina} @csail.mit.edu
Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VITAMINC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VITAMINC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness—improving accuracy by  $10\%$  on adversarial fact verification and  $6\%$  on adversarial natural language inference (NLI). Moreover, the structure of VITAMINC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.<sup>1</sup>
Determining the truthfulness of factual claims by comparing them to textual sources of evidence has received intense research interest in recent years. An underlying, but often overlooked, challenge for this paradigm, however, is the dynamic nature of today's written resources. An extraordinary amount of new information becomes available daily; as a result, many consequential facts are established, changed, or added to over time. We argue that the quality of fact verification systems should be
Figure 1: In VITAMINC, we focus on Wikipedia revisions in which the factual content changes. This example revision now supports an initially refuted claim.
measured by how well they adjust to new evidence. In this way, we seek to advance fact verification by requiring that models remain reliable and robust to the change present in practical settings.
To this end, we focus on fact verification with contrastive evidence. That is, we infuse the standard fact verification paradigm with challenging cases that require models to be sensitive to factual changes in their presented evidence (hereon referred to interchangeably as "context"). We present VITAMINC, $^2$  a new large-scale fact verification dataset that is based on factual revisions to Wikipedia. The key concept is exemplified in Figure 1: there a factual revision yields a contrastive pair of contexts that are nearly identical in language and content—except that one context refutes the given claim, while the other supports it.
This type of contrastive structure exposes existing deficiencies in model behavior. To illustrate this, we train a classifier on the popular FEVER fact verification dataset (Thorne et al., 2018) and evaluate it on contrastive claim-evidence pairs. We find that the model flips its prediction from the original verdict on only  $56\%$  of the contrastive cases. When examples from VITAMINC are included during training, however, the model's sensitivity increases, flipping on  $86\%$  of contrastive cases.
Such context-sensitive inference has two main benefits. First, it ensures that the model consid-
$^{2}$ Etymology of VITAMINC: Contrastive evidence keeps fact verification models robust and healthy, hence "Vitamin C."
<sup>1</sup>The VITAMINC dataset and our models are available at: https://github.com/TalSchuster/VitaminC
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 624-643 June 6-11, 2021. ©2021 Association for Computational Linguistics
ers the provided evidence rather than relying on built-in static knowledge, such as that obtained via language model pre-training (Petroni et al., 2019; Roberts et al., 2020). This is particularly important for scenarios in which the source of truth is mutable (e.g., the current US president, or new declarations as in Figure 1). Second, this setting discourages certain biases and idiosyncrasies—such as exploiting differences in how true vs. false claims are posed—that are common in similar crowd-sourced datasets (Poliak et al., 2018; Schuster et al., 2019). Indeed, we show that augmenting both fact verification models and NLI models with VITAMINC data improves their robustness to adversarial inputs.
Furthermore, our emphasis on contrastive contexts allows us to expand on the scope of commonly considered tasks. Most of the fact verification literature focuses on resolving claims to be true or false (Popat et al., 2018; Thorne and Vlachos, 2018; Wang, 2017). The surrounding ecosystem, however, includes additional challenges, some of which we explore here: Documents such as Wikipedia articles are updated frequently; which edits represent factual changes? For a given claim and (refuting or supporting) evidence pair, which words or phrases in the evidence are most relevant? If we know that a certain claim is true, can we modify an out-dated document to be consistent with it? We show that the unique structure of our VITAMINC dataset can be leveraged to provide both supervised and distantly supervised data for these new questions.
1. We pose a contrastive fact verification paradigm that requires sensitivity to changes in data;
2. We introduce VITAMINC, a new large-scale dataset that supports this paradigm;
3. We demonstrate that training on VITAMINC leads to better performance on standard tasks;
4. We show how VITAMINC opens the door to additional research directions in fact verification.
Fact Verification. The FEVER dataset (Thorne et al., 2018) fueled the development of many fact-checking models (e.g., see Hanselowski et al., 2018; Nie et al., 2019a,b; Yoneda et al., 2018, inter alia). The claim creation process, however, required crowd-workers to write claims related to Wikipedia articles, and was found to engender biases that allow an evidence-agnostic model to
achieve unexpectedly high performance (Schuster et al., 2019). Other recent datasets cover verification against tables (Chen et al., 2020), relational databases (Jo et al., 2019), Wikipedia references (Sathe et al., 2020), multiple articles (Jiang et al., 2020), and search snippets (Augenstein et al., 2019). These resources all assume static ground truths. In contrast, VITAMINC compares objective claims to a dynamic source of truth, and requires models to change their verdicts accordingly.
Annotation Bias. Annotation artifacts are common in many NLP datasets, and affect performance on adversarial and contrastive examples (Gardner et al., 2020; Ribeiro et al., 2020; Ross et al., 2020). Sentence-pair inference tasks such as fact verification (Paul Panenghat et al., 2020; Schuster et al., 2019) and NLI (Gururangan et al., 2018; McCoy et al., 2019; Poliak et al., 2018; Tsuchiya, 2018) are no exception. Alleviating this bias requires either modeling solutions (Karimi Mahabadi et al., 2020; Pratapa et al., 2020; Shah et al., 2020; Thorne and Vlachos, 2020; Utama et al., 2020b), which have limited effectiveness (Utama et al., 2020a), or adversarily removing troublesome training examples (Bras et al., 2020) or manually collecting new ones (Nie et al., 2020; Thorne et al., 2019a), which is model specific. Instead, our dataset design avoids single-sentence artifacts and provides model-agnostic challenging examples that increase the robustness of trained models.
Explainability. Current fact verification datasets provide sentence-level rationales (DeYoung et al., 2020; Petroni et al., 2020) but do not enforce the model's verdict to rely on them—leading to a potential discrepancy. VITAMINC ensures the verdict is conditioned on the retrieved evidence. Moreover, we use the revision history as distant supervision for word-level rationales, allowing for finer-grained explanations (Camburu et al., 2018; Lei et al., 2016; Portelli et al., 2020; Thorne et al., 2019b).
Factually Consistent Generation. Generating texts that match given facts is a known challenge (Fan et al., 2020; Kryscinski et al., 2020; Lewis et al., 2020b; Parikh et al., 2020; Shah et al., 2020; Tian et al., 2020) as language models tend to degenerate and hallucinate (Holtzman et al., 2020; Schuster et al., 2020; Zhou et al., 2020). Moreover, evaluation is non-trivial, and usually manual. VITAMINC includes supervised data for training sequence-to-sequence models, and provides auto
matic evaluation via the fact verification classifier.
3 The VITAMINC Dataset
VITAMINC (abbreviated VitC) is based on revisions to English Wikipedia. Wikipedia has become a comprehensive online resource that is rigorously maintained by a large and active community (Benjakob and Harrison, 2019). While adversaries do try to insert disinformation, popular pages are usually quickly corrected (Kumar et al., 2016). Furthermore, Wikipedia's policies dictate that its content should be written from a neutral perspective—or should otherwise objectively state all points of view.<sup>3</sup> These properties make Wikipedia a suitable source of evidence for fact verification models. In the following section, we outline our process for mining factual revisions from Wikipedia.
3.1 Collecting Factual Revisions
We collected the 5K most-viewed English Wikipedia articles<sup>4</sup> as of January 2020, along with any additional articles referred from them (on average 100 per article). We also included all articles from the FEVER dataset (Thorne et al., 2018). For each article, we retrieved up to 500 of its most recent revisions. In May 2020, we added all COVID-19 related articles<sup>5</sup> and all of their 41K revisions at the time. Combined together, this resulted in a total of  $\sim 200$  million revisions. For each revision, we identified all of the modified sentences and stored two versions: (1) before, and (2) after the edit.
In our task, we are only interested in edits made with an intent to introduce a factual modification—i.e., a change for which one can make a claim that is supported by one sentence, but not by the other.6 To expedite annotation, we trained a BERT classifier (Devlin et al., 2019) on a small labeled set of revised sentences determined to be factual (Yang et al., 2017), and used this model to select the top 305K edited sentences from the corpus for manual annotation. Trained human annotators were then presented with the sentence pairs, and were asked to mark the ones that indeed represented a factual change. Sentences lacking self-contained context were filtered (e.g., short expressions from tables or bulleted lists). Example annotations are presented in Table 1. Note that these annotations can also be
recursively recycled for re-training the automated BERT classifier in the future to expand the corpus further (we also introduce this as a task, see §4.1).
3.2 Writing Claims
The factual Wikipedia revisions guide us in creating challenging claims for fact verification. For each revision, annotators were asked to write two symmetric claims related to the same edit:
1. The first should be supported by the original sentence and refuted by the revised sentence;
2. The second should be supported by the revised sentence and refuted by the original sentence.
When an explicit contradiction was not possible, a not enough information (NEI) relation was used. A group of 70 native English speakers<sup>7</sup> wrote and reviewed claims. During the annotation period, annotations were delivered in weekly batches, from which we examined random samples to provide feedback and request corrections. Annotators were instructed to write short and self-contained claims. Furthermore, annotators were instructed to avoid copying exact phrases and values when possible, in order to avoid a bias for substantially higher word overlap in supporting pairs over refuting pairs. For example, rather than stating, “there are  $\underline{x}$  confirmed cases of coronavirus in the US”, one can write “there are more than  $\underline{z}$  confirmed cases of coronavirus in the US”, which is supported if  $x > z$  and refuted otherwise. For revisions that only add new information or that remove outdated facts without replacing them, annotators wrote a single claim.
3.3 Adding Synthetic Revisions
Naturally, the real Wikipedia revisions we collect mostly describe facts that frequently change over time, or that are prone to mistakes and corrections (such as quantitative values, see Appendix A.1) (Faruqui et al., 2018; Yang et al., 2017). Sensitivity to contrastive contexts, however, is desirable behavior for any claim. This can both ensure consistency with external sources of truth, and improve the model's faithfulness via connecting the verdict with a specific evidence (Jacovi and Goldberg, 2020; Ross et al., 2020). For example, we require the model to not only classify the claim "Tom Hanks was honored by a president" as true, but to also change its verdict to false if paired with a (fictional) contrasting evidence. As a result, we can verify that the model prioritizes sentence-pair inference over
<sup>7</sup>We sourced our annotators through TransPerfect.
<https://bit.ly/Wiki_Neutral_POV>
4https://bit.ly/Wiki_poplar_pages
<sup>5</sup>https://wikimediafoundation.org/covid19
6 Many edits only reflect grammatical corrections, paraphrasing, or "Wikification" (text formatting/page linking).
Factual	Wikipedia sentences before and after a revision, presented with VITAMINC claims if the revision is factual.
X	Before	More stringent actions were taken in China once the seriousness of the outbreak became apparent, such as quarantining entire cities affecting 60 million individuals in Hubei, and strict travel bans.
After	More drastic actions were taken in China once the severity of the outbreak became apparent, such as quarantining entire cities affecting 60 million individuals in Hubei, and strict travel bans.
✓	Before	In animals, spaying involves an invasive removal of the ovaries, but rarely has major complications other than that spayed animals tend to gain weight.
After	In animals, spaying involves an invasive removal of the ovaries, but rarely has major complications; the superstition that it causes weight gain is not based on fact.
Claim 1	Spayed animals gain weight.
Claim 2	Weight gain in spayed animals is a superstitious myth.
X	Before	As of 16 March, more than 182,000 cases of the disease have been reported in over 160 countries and territories, resulting in around 79,000 recoveries and more than 7,100 deaths.
After	As of 16 March, more than 182,000 cases of the disease have been reported in over 160 countries and territories, resulting in more than 7,100 deaths and around 79,000 recoveries.
✓	Before	Global hybrid sales are led by the Prius family, with sales of 4.7 million units representing 66.8% of TMC worldwide sales of 7.053 million Lexus and Toyota units through September 2014.
After	Global hybrid sales are led by the Prius family, with sales of 5.264 million units representing 65.4% of TMC worldwide sales of 8.048 million Lexus and Toyota units delivered through July 2014.
Claim 1	Prius sold less than 5 million units, representing over 65.5% of TMC worldwide sales.
Claim 2	Prius sold more than 5 million units, representing less than 65.5% of TMC worldwide sales.
memorization, which can help it generalize better. Therefore, we use the FEVER dataset to augment VITAMINC with synthetic revisions to Wikipedia sentences.
We follow the setting of Schuster et al. (2019) to expand claim-evidence pairs from FEVER (Thorne et al., 2018). Specifically, given a false claim from FEVER, we ask annotators to edit the sentence that refutes it so that it will then support the originally false claim. Additionally, we ask them to write a new claim that is refuted by the new, modified sentence, but that is supported by the original version. Following this method, we obtain two claims where each can be supported or refuted by the original, or the synthetically revised, sentence. We follow the same process for constructing synthetic examples using true claims, but with flipped labels.
In total, 304,671 revised Wikipedia sentences were examined by annotators, of which 107,056 (35%) were found to express a factual modification and were passed to the group of expert annotators for claim writing. As two symmetric claims with opposing facts were created (when possible) for each revision, this resulted in a total of 325,724 total claim-evidence pairs. We collected 163,180 addi
Table 1: Examples of non-factual revisions vs. factual revisions, and the claims associated with the later. Factual updates change the outcome (i.e., true or false) of a claim that might be in question. Accordingly, the verdict of a classifier should change based on the version presented. Modified words are underlined and colored.
Split	Supports	Refutes	NEI
Real	Syn	Real	Syn	Real	Syn
Train	124,864	60,850	71,108	60,850	52,981	-
Dev	21,102	10,382	12,146	10,382	9,042	-
Test	17,306	10,358	9,907	10,358	7,268	-
Table 2: Number of claim-evidence pairs in VITAMINC. Breakdowns of real vs. synthetic revisions are presented on the left and right of each cell, respectively.
tional pairs following the synthetic process. The data was partitioned as shown in Table 2. The assignment was done randomly by article, and is consistent with FEVER for overlapping articles. Appendix A contains additional details.
4 VITAMINC Tasks
The unique structure of VITAMINC allows us to derive annotations that provide a novel source of supervision for several fact-verification-related tasks. We describe the four main tasks we consider in this work, along with baseline models: (1) factual revision flagging, (2) fact verification, (3) word-level rationales, and (4) factually consistent generation. Figure 2 illustrates an example from VITAMINC. We use the following notations:
-  $\mathcal{C}$  is the space of short sentences that express an arbitrary factual statement that can potentially be verified or debunked by external sources.
1. Factual Revision Flagging
The revision  $(s_{t - 1}, s_t)$  is factual because it modifies when the first COVID-19 case was identified
2. Fact Verification
The following claim is refuted by  $s_{t-1}$  and supported by  $s_t$ :
"COVID-19 outbreak was identified before December"
Figure 2: The VITAMINC dataset uses Wikipedia revisions to motivate four central fact verification tasks. Revision source: https://en.wikipedia.org/wiki/?diff=945689803.
3. Word-level Rationales
The anchoring words in  $s_t$  that support
the claim are:
"first identified [...] in 17 November 2019 [...]
4. Factually Consistent Generation
The contradictory  $s_{t-1}$  can be revised to state:
"The outbreak [...] before December 2019 [...]
-  $S$  is the space of sentences that can be found in a trusted online resource (Wikipedia in this study).
-  $(s_{t - 1}, s_t)$  denotes the two versions of a sentence that was revised from  $s_{t - 1}$  to  $s_t \in S$ .
-  $\operatorname{rel}(c, s)$  denotes the relation between the claim  $c \in \mathcal{C}$  and observed evidence  $s \in \mathcal{S}$ —which can either support  $c$  (SUP), refute it (REF), or not contain enough information (NEI).
4.1 Factual Revision Flagging
Online resources like Wikipedia are continuously changing. In order to remain a reliable and neutral source for recent information, its active community of users must constantly verify and correct the revisions of others. We define factual revision flagging as the task of identifying revisions that introduce a factual change—e.g., by either modifying a certain fact, adding a new one, or removing an existing one. Such an automated detection process can help the community moderate important articles by serving as a watchdog for factual revisions. Furthermore, tracking factual revisions to certain articles can potentially help keep reliant articles consistent (e.g., citing articles, or non-English versions).
We pose factual revision flagging as a binary classification function  $f_{\mathrm{flag}} \colon S \times S \to \{0,1\}$ , where for a revision  $(s_{t-1}, s_t)_i$ , we set  $y_i = 1$  iff there exists a claim in  $C$  whose label (SUP or REF) changes as a result of the edit (i.e., SUP → {REF, NEI} or REF → {SUP, NEI}). Table 1 provides example factual and non-factual revisions. We evaluate the following baseline models:
Edit Distance. We measure the edit distance between  $s_{t-1}$  and  $s_t$ , assuming that larger edits are more likely to represent substantive changes. We tune a decision threshold on the validation set.
BOW. We use an MLP on top of a bag-of-words representation. Each sentence is encoded as  $e_*$ , the average fastText (Bojanowski et al., 2017) word embedding of its edited words (i.e., that were removed or modified in the revision). The MLP input is then taken as  $[e_{t-1}; e_t; |e_t - e_{t-1}|; e_t \cdot e_{t-1}]$ .
ALBERT. We train the ALBERT transformer (Lan et al., 2020) using either only the edited words (diff), or the full sentence pair (full).
Our basic setting is similar to the inference task of the FEVER dataset. We predict the verdict for a claim given an observed evidence,  $f_{\text{verdict}} \colon \mathcal{C} \times \mathcal{S} \to \{\text{SUP}, \text{REF}, \text{NEI}\}$ . The FEVER dataset, however, contains independent claim-evidence pairs. In our setting, we have claims paired with revisions such that  $\text{rel}(c_i, s_{t-1}) \neq \text{rel}(c_i, s_t)$ , creating contrastive triplets. For example, the claim in Figure 2 states that the COVID-19 outbreak was identified before December. VITAMINC matches it with two different contexts (before and after the presented revision), that can either support or refute that claim.
Our baseline model is an ALBERT sentence-pair classifier that predicts  $\operatorname{rel}(c, s)$ . Compared to BERT (Devlin et al., 2019), it uses fewer parameters by shrinking the embedding size and sharing layers, which we find to improve robustness.
4.3 Word-level Rationales
Word-level rationales provide useful explanations for predictions of neural models (Lei et al., 2016). Such explanations can be particularly useful for semi-automated fact verification, since they allow users to quickly interpret and trust the model's verdict. In Figure 2, for example, the date of the first identified case can explain the verdict for the claim.
As first proposed by Lei et al. (2016), the standard definition of extractive rationales asks for selecting the minimal set of input tokens that is sufficient for preserving the model's prediction. Here we use a slightly modified definition following Shah et al. (2020), where we identify the minimal set of evidence tokens where removing them
To focus on the inference task, as opposed to a full end-to-end system, we assume that we have access to an oracle retriever.
Roitero et al. (2020) showed that explanations can increase the agreement between users and expert fact-checkers.
will change the input's label to NEI.
We pose this task as conditional masking, where we learn a function  $f_{\text{rationale}} \colon \mathcal{C} \times \mathcal{S} \to \{0,1\}^n$ , where  $n$  is the length of an evidence  $s \in \mathcal{S}$ . Given an evidence  $s = (x_1, \ldots, x_n)$  and a claim  $c$ , where  $\operatorname{rel}(c, s) \in \{\operatorname{SUP}, \operatorname{REF}\}$ , we want to find a mask  $m$  such that  $\operatorname{rel}(c, s \odot m) = \operatorname{NEI}$ , where
$$
s \odot m = \left\{ \begin{array}{l l} x _ {i} & \text {i f} m [ i ] = 0; \\ <   \text {m a s k} > & \text {i f} m [ i ] = 1. \end{array} \right.
$$
Moreover, we want  $m$  to be as sparse as possible. Intuitively,  $s \odot m$  could be viewed as an incomplete revision in which the masked words that have not yet been filled in will determine the relation with the claim. We say that  $m$  reveals the most responsible words in  $s$  for resolving  $c$ . Following Shah et al. (2020), we formulate an unsupervised objective as
$$
\min  \sum_ {i = 1} ^ {n} m _ {i} \text {s . t .} \operatorname {r e l} (c, s \odot m) = \mathrm {N E I}. \tag {1}
$$