SlowGuess/ACL_2020_OCR huggingface dataset:
The first step thereof, is to link the same sources detected in the text with the same statement. Since the sources can be a url or a mention of an entity, we do wikification (Ratinov et al., 2011; Cheng and Roth, 2013) for the extracted sources. Specifically, to wikify a source mention, we first adapt a redirect-based wikification method (RedW) (Shnayderman et al., 2019), which is efficient and context free. Besides Wikipedia redirects, we also include the value of the attribute website as a candidate mention of the entity if it exists, for example nytimes.com for The New York Times. Then we compute the text similarity between the source mention and the other mentions that have already been linked, and eventually map the source mention to the entity in Wikipedia with a similarity score higher than a threshold. Our similarity score is a linear combination of lexical similarity (Do et al.) between the source mention to (1) candidate mentions produced by RedW and (2) mentions linked. To determine the same statement, we allow an approximate match by computing the cosine similarity with their ELMo representations.
The second step is to decide the relationship between the statements. In this work, we include the relations, i.e., identical, paraphrased, textually entailed and contradicted. Determining if the two statements are identical is straightforward, and we collect parallel sentences (Ganitkevitch et al., 2013; Thorne et al., 2018) to fine-tune classifiers (Devlin et al., 2018) to determine other relations.
4 Application: Claim Verification
We take claim verification as an example to demonstrate the importance of claim provenance graph. Concretely, we elaborate how we can use the graph to improve the estimation of claim veracity.
4.1 Claim Evidence Graph
Claim provenance graph is to help us understand where the claim may come from and how it may be disseminated over time. The nodes of the graph represent the sources with the statements they made, and the edges represent the relations between the statements. However, when doing claim verification, we also care about the direct relation between the statement made by the source and the given claim. Therefore, we derive a claim evidence graph from the claim provenance graph based on which we do claim verification. Specifically, we keep the nodes and edges in the claim provenance graph,
and add another label on each edge with one of support, contradiction and neutral. The new label on the edge represents the opinion of the source to the given claim, whose generation can be viewed as a regular textual entailment problem.
4.2 Boosting Claim Verification
Given a claim, the most straightforward way to do claim verification is voting by the opinion of different sources. Without the graph, typically we can first search for related articles for the given claim, then collect their opinions and vote. Since each article has its own opinion, we can determine the veracity of the claim by the majority vote of the opinions by those articles. However, an article can include multiple different statements about the same claim with different opinions, and multiple articles can refer to the same statement about the claim from a common source. Therefore, the majority vote by opinions in article level is not good enough, since it suffers from (1) opinions which are too coarse-grained and (2) overcounting the opinions from the same source, which is also known as collusion or dependency of sources problem in truth finding (Pochampally et al., 2014).
Luckily, with the claim evidence graph, we can collect the opinions in statement level, and vote the veracity by sources that are more independent with each other. Specifically, given an evidence graph of a claim, we start with the sink node and do breadth first search to find all source nodes whose indegree are 0, and leverage those sources to vote by their opinions to get an estimation of the claim veracity. To distinguish between sources and source nodes of the claim evidence graph, we call sources, a.k.a all nodes of the graph all-sources, and independent sources, a.k.a all source nodes of the graph, prov-sources. In this case, we can leverage prov-sources that are not dependent with each other to vote. This strategy can also be used to choose sources that will be fed to other source-aware fact finding models (Pasternack and Roth, 2013).
We evaluate (1) the solutions to infer the provenance graph, and (2) the effectiveness of the claim evidence graph on claim verification, which is adapted from the inferred provenance graph. For each goal, we first elaborate the experimental set-
tings, and then describe the results and analysis  ${}^{3}$  .
5.1 Claim Search and Source Extraction
To evaluate the methods inferring the provenance, we focus on the performance of claim search and source extraction by looking at if the method can extract the sources accurately and exhaustively.
DataSet In this experiment, we use MPQA  $2.0^{4}$  (Choi et al., 2005) as the corpus to train and test our models. The dataset consists of 535 documents that have been manually annotated with opinion related information including sources. For example, given a piece of text "... According to Malan, the dollarization process is irreversible ..." "Pedro Malan" is annotated that it has an opinion on "the dollarization process is irreversible". Note that a single claim can be annotated with multiple sources including the writer of the text, and each source except the writer is a span of text in the given text. MPQA dataset is originally developed for identifying sources for the given opinion, and the opinion sometimes can be a noun phrase or an entity, while in our problem we are to extract sources for claims. Therefore, we only leave the opinions which are sentences as the query claim, and perform 10-fold cross validation to evaluate the performance of our models and the baselines.
To evaluate the performance, we compute precision, recall and F1 score with overlap match, which means we consider the returned source correct, if it overlaps with at least half of the words of the corresponding annotated source.
Models and Baselines We view source extraction as an IE problem and tackle it by TE models. According to Section 3.2, we evaluate the performance of our model with different versions. The first one is the vanilla TE model, which is fine-tuning BERT to determine if the source makes the claim given the context, i.e., if  $a_i^d$  entails  $b_i^d[s]$ , denoted as TE-V. The second one is the pairwise TE model, which is fine-tuning BERT with two objectives as described in Section 3.2, denoted as TE-P. The third one is the pairwise TE model with the incremental training data provided by an off-the-shelf SRL tool (He et al., 2018), denoted as TE-D.
In terms of the baselines, we compare our models with (1) the sequential tagging solution, which is fine-tuning BERT to predict if the token in the
3Our code is available at https://cogcomp.seas.upenn.edu/page/publication_view/901.
<sup>4</sup>http://mpqa.cs.pitt.edu/
text is part of the source, denoted as SEQ; (2) TE model with semantic role labeling, which is to predict if the ARG1 labeled by the SRL is a paraphrase of the query claim, denoted as TE-S.
PRECISION	RECALL	F1
AVG	STD	AVG	STD	AVG	STD
SEQ	0.4906	0.056	0.3373	0.064	0.3998	0.060
TE-S	0.6918	0.053	0.6124	0.048	0.6459	0.022
TE-V	0.7282	0.038	0.7103	0.064	0.7165	0.033
TE-P	0.7249	0.024	0.7877	0.065	0.7538	0.038
TE-D	0.7240	0.028	0.8125	0.048	0.7645	0.024
Table 1: The performance of different models on source extraction. In this table, we report the average precision, recall and F1 score of the 10-fold cross validation on MPQA with their corresponding standard deviations.
Results We report the source extraction results of different methods in Table 1. As shown in the table, modeling source extraction as a TE problem can achieve a better performance than modeling the problem as a sequential tagging task, since both precision and recall of SEQ are lower than the ones of TE-S, which obtained the lowest precision and recall among all of the TE methods. We think the reason is that doing sequential tagging well may need to capture the syntactic relationship in the sentences, while only annotating the source is not enough to make the model understand it.
Comparing TE-S with other TE based models, we can observe that leveraging off-the-shelf SRL to produce candidate sources is helpful. However, determining the sources based on the entailment relationship between ARG1 and the claim will introduce noise, and the quality and the deficiency of the SRL then becomes a bottleneck. Thus, TE-V is better than TE-S. Furthermore, as we argued in Section 3.2, incorporating margin ranking loss into the objective function can help learn the discriminate feature better, which is reflected by the better performance achieved by TE-P compared to the performance of TE-V. We can also observe that incremental training can further improve the performance, as TE-D achieves the best F1 score.
5.2 Claim Verification
In this experiment, we evaluate if the provenance graph can help claim verification methods by its derived claim evidence graph.
DataSet We crawl all 495 fact check questions listed on www.factcheck.org/askfactcheck/ as the set of query claims, and annotate true or false for each claim based on its conclusion shown on the webpage. Note that we remove the fact check
questions without a consolidate conclusion or asking why or what questions about the claim. We also crawl the short answer section, which is a summarized sentence to support the conclusion of the fact check question, listed on the webpage. We use the sentence as the premise, the claim as the hypothesis, and the annotated label as the label, to fine-tune a textual entailment model (Devlin et al., 2018) that can help us determine the label of the edge in the claim evidence graph.
Models and Baselines For each claim, we search it by google search  $^{5}$ , and obtain the articles from the top-10 links as the corpus to extract the sources and construct the provenance graph.
Given the provenance graph, we transform it to a claim evidence graph using our fine-tuned model. Then, we implement two methods for claim verification: majority vote, and Simple LCA (Pasternack and Roth, 2013). Note that Simple LCA is iteratively estimating the trustworthiness of the sources and the veracity of the claims. As described in Section 4, we feed the two methods with prov-sources obtained from the claim evidence graph, denoted as Prov-Src. For comparisons, we (1) feed the top-10 links directly as sources into majority vote and Simple LCA respectively; this baseline is denoted as Doc; (2) feed all-sources of the claim evidence graph into majority vote and Simple LCA, denoted as All-Src. Note that All-Src only leverages the nodes of the provenance graph, while Prov-Src leverages both the nodes and the structure of the provenance graph.
To compare the performance, we compute the accuracy of the estimation of the claim veracity.
Figure 4: The performance of claim verification with majority vote and Simple LCA. For each method, we evaluate the performance with sources based on articles, all nodes and source nodes of claim evidence graph respectively.
5https://pypi.org/project/google/
remove the link from www.factcheck.org
Results In Figure 4, we report the accuracy of both algorithms, majority vote and Simple LCA, with three groups of sources. Our results show that for both majority vote and Simple LCA, leveraging the claim evidence graph leads to a better performance when compared with using articles as sources. It demonstrates that using articles as sources is too coarse-grained for claim verification, and thus it is very likely to be biased. The evidence graph provides the models with evidence from more sources (All-Src) and sources that are more likely to be independent (Prov-Src), thus improves the performance.
To the best of our knowledge, our work is the first to formally define and propose a framework to infer the provenance graph of given claims made in natural language. One line of the related work includes identifying sources of opinions in opinion analysis (Choi et al., 2005) and quote attribution (Muzny et al., 2017; Pavllo et al., 2018), which is related to one of the components we use to infer the provenance graph. Earlier work performs information extraction via sequential tagging in a given text and collects paired sources and opinions or quotes and speakers. We do not detect all quotes or opinions stated in the text, but rather detect the sources generating statements related to the given claim, whether it is described implicitly or explicitly in the text. Furthermore, we also construct a graph that depicts the history of how a claim has disseminated over time, a task that was not addressed in earlier work.
Another line of related work includes fact-checking (Thorne et al., 2018; Thorne and Vlachos, 2018; Zhang et al., 2019) and claim verification (Popat et al., 2017, 2018). However, those works focus only on capturing discriminative linguistic features of misinformation, while we argue that determining the provenance of claims is essential for addressing the root of the problem, understanding claims and sources.
We introduce a formal definition and a computational framework for the provenance of a natural language claim given a corpus. We argue that this notion of provenance is essential if we are to understand how claims evolve over time, and what sources contributed to earlier versions of the claims.
We provide initial results exhibiting that our framework can be used successfully to infer the provenance graph and, that it can be applied to boost the performance of claim verification.
The framework introduces a range of important questions both from the inference and the application perspectives. For example, inferring the current version of the provenance graph depends on the ability to identify authors. This could be difficult when the authors are not mentioned in the text, which might require a deeper understanding of sources' writing style and positions.
From the application perspective, it is clear that the graph contains more information than we have exploited so far. For example, the edge labels, indicating the evolution operators of a claim should also be useful. In particular, this will support a more informed study of influence of specific sources and of trustworthiness, and possibly other aspects of information spread.
The authors would like to thank Nitish Gupta and the anonymous reviewers for insightful comments and suggestions. This work was supported in part by a Focused Award from Google and by IARPA Contract No. 2019-19051600006 under the BETTER Program. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.
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