Fact Checking with Insufficient Evidence (2022)

Abstract: Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions only when there is sufficient evidence and otherwise indicate when it is not enough. To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions. First, we conduct an in-depth empirical analysis of the task with a new fluency-preserving method for omitting information from the evidence at the constituent and sentence level. We identify when models consider the remaining evidence (in)sufficient for FC, based on three trained models with different Transformer architectures and three FC datasets. Second, we ask annotators whether the omitted evidence was important for FC, resulting in a novel diagnostic dataset, SufficientFacts1, for FC with omitted evidence. We find that models are least successful in detecting missing evidence when adverbial modifiers are omitted (21% accuracy), whereas it is easiest for omitted date modifiers (63% accuracy). Finally, we propose a novel data augmentation strategy for contrastive self-learning of missing evidence by employing the proposed omission method combined with tri-training. It improves performance for Evidence Sufficiency Prediction by up to 17.8 F1 score, which in turn improves FC performance by up to 2.6 F1 score.

## introduction computational fact checking approaches typically use deep learning models to predict the veracity of a claim given background knowledge @xcite @xcite . however, the necessary evidence is not always available, either due to incomplete knowledge sources, or because the claim has newly emerged and the relevant facts are not documented yet. in such cases, fc models should indicate that the information available is insufficient to predict the label, as opposed to making a prediction informed by spurious correlations. prior work shows that fc models can sometimes predict the correct veracity based on just the claim, ignoring the evidence, and that they can overly rely on features such as the word overlap between the evidence and the claim @xcite , leading to biased predictions. however, there are no previous studies on what evidence a fc model considers to be enough for predicting a veracity label. to this end, this work introduces the novel task of evidence sufficiency prediction illustrated in figure 1 , which we define as the task of identifying what information is sufficient for making a veracity prediction. this task is related to fc and can operate on instances and models from fc datasets, but is focused on evaluating the capability of models to detect missing important information in the provided evidence for a claim. the latter is usually not evaluated explicitly in current fc benchmarks, where joint scores disregard a fc model's prediction when insufficient evidence is retrieved. we study the new task by, first, conducting a thorough empirical analysis of what models consider to be sufficient evidence for fc. second, we collect human annotations for the latter, which results in a novel diagnostic dataset, sufficient-facts, for fc with omitted evidence. finally, we employ the method introduced for the empirical analysis to improve the performance of models on the new task of evidence sufficiency prediction, and show that considering it a component task of fc significantly improves fc performance. for the empirical analysis, we propose a new fluency-preserving method that occludes portions of evidence, automatically removing constituents figure 1 : an example from the vitaminc test set, where the number modifier has been omitted from the evidence. this results in there not being enough evidence for predicting its support for the claim as judged by human annotators, while two of the models still find the remaining evidence to be sufficient. or entire sentences, to create incomplete evidence. we provide those as input to an ensemble of transformer-based fc models to obtain instances on which fc models agree vs. disagree to have (in)sufficient information. we perform extensive experiments with three models-bert @xcite , roberta @xcite , and albert @xcite -and three textual fc datasets with different types of claims-fever @xcite , hover @xcite , and vitaminc @xcite . to compare model behavior with human rationales for evidence sufficiency prediction, we ask annotators to indicate if the occluded evidence texts still provide enough information for a factcheck. this results in a novel diagnostic test dataset, sufficientfacts, which contains information about the type of the omitted information, allowing for in-depth analyses of model behavior. finally, to improve model performance for detecting omitted important evidence and, in turn, fc, we propose to combine the proposed evidence omission method with tri-training @xcite , which utilizes the agreement of three different machine learning models to label unlabeled training instances ( §5). this results in a novel counterfactual data augmentation schema for learning of (in)sufficient information. we find that the proposed approach is highly effective in improving model performance by up to 17.8 f 1 score on the newly introduced sufficientfacts. this also leads to improvements of up to 2.6 f 1 score on the standard fc test sets for the corresponding datasets. ## datasets we employ three fact checking datasets (see vitamin c 370,653 train 63,054 dev 55,197 test label: supports (∈ {supports, refutes, not enough info}) claim: westlife sold more than 1 m. video albums and made over 23.5 m. sales in the uk. evidence: @xcite according to the british phonographic industry (bpi), westlife has been certified for 13 m. albums, 1.3 m. video albums, and 9.8 m. singles, with a total of more than 24 m. combined sales in the uk. hover 18,171 train 1818 dev 4,000 test label: not supported (∈ {supports, not @xmath0: reason is treason is the second single release from a british rock band that are not from england. the band known for the early 90's album novelty are not from england either. evidence: [kasabian] kasabian are an english rock band formed in leicester in 1997. [jawbox] jawbox was an american alternative rock band from washington, d.c., united states. [reason is treason] ''reason is treason'' is the second single release from british rock band kasabian. [novelty (album)] novelty is an album from the early 90's by jawbox. fever @xcite ) contains claimevidence pairs, where the evidence consists of sentences from wikipedia pages, and the claims are written manually based on the content of those wikipedia pages. note that 87% of the claims have evidence consisting of one sentence. the dataset has a high ratio of token overlap between the claim and the evidence, where the overlap is naturally higher for claims that are supporting (69%), than refuting (59%) and nei (54%) claims. the high overlap ratio can create a bias for learning from token overlap, which can further prevent generalisation, as also noted in related work @xcite . vitamin c @xcite is a collection of sentences from wikipedia containing factual edits. for each factual edit, annotators construct a claim that is supported and one that is refuted with the old and the new version of the evidence. when the factual edit introduces/removes facts from the evidence, claims are constructed so that there is not enough information (nei) to support them. due to its contrastive nature and reduced claim-evidence overlap, the authors demonstrate that models trained on the dataset gain a 10% accuracy improvement on adversarial fact verification. hover @xcite ) is designed to collect claims that need several hops over wikipedia evidence sentences to verify a claim. the evidence contains between two and four sentences from different wikipedia articles. as the test dataset is blind and we use the gold evidence, we use the development set for testing purposes and randomly select 10% of the training dataset for development. ## evidence omission to study what types of information the evidence models consider important, we propose to conduct causal interventions for the evidence by omitting information from it. we hypothesize that removing information important for the model to predict the support of evidence for a claim will cause a change in its original prediction, leading to the model indicating that there is missing information. if the removed information is not important for the model though, removing it would not change the model's prediction. we then ask whether the information that is important for a model when predicting the support of the evidence text for a claim, is actually important as judged by human annotators. the human annotations allow for a systematic study of common model errors, that is, when the models still predict the correct label even if important evidence information has been type l claim evidence s r the endless river is an album by a band formed in 1967. [[the endless river]] the endless river is a studio album by pink floyd. [[pink floyd]] pink floyd were founded in 1965 by students . . . pp r uranium-235 was discovered by arthur jeffrey dempster in 2005. [[uranium-235]] it was discovered in 1935 by arthur jeffrey dempster. nounm s vedam is a drama film. [[vedam (film)]] vedam is a 2010 indian drama film written and directed by radhakrishna jagarlamudi . . . adjm s christa mcauliffe taught social studies. [[christa mcauliffe]] she took a teaching position as a social studies teacher at concord high school. . . advm s richard rutowski heavily revised the screenplay for natural born killers. [[natural born killers]] the film is based on an original screenplay that was heavily revised by writer david veloz, associate producer richard rutowski . . . numm s being sentenced to federal prison is something that happened to efraim diveroli. [[efraim diveroli]] diveroli was sentenced to four years in federal prison . datem r colombiana was released 1st october 2001. [[colombiana]] colombiana is a french action film from 1st october 2011 . . . sbar r north vietnam existed from 1945 to 1978. [[north vietnam]] north vietnam, was a state in southeast asia which existed from 1945 to 1976. ## evidence omission detection to improve the performance of models in recognizing when the evidence is not enough for verifying a claim, we experiment with cad ( §5.2) and a cl loss ( §5.1). both methods use contrastive data augmented with the proposed evidence omission method ( §4.1) in combination with tritraining, as illustrated in figure 3 . we omit information from the original (anchor) evidence to collect potential negative instances with missing important evidence information compared to the original evidence (figure 3 , right). from the resulting candidates, we select as negative only those predicted as having insufficient information by the other two supervised models from the ensemble ( §4) (e.g., roberta and albert predict nei when we are training a model with a bert transformer architecture). we also collect positive instances that still have sufficient evidence information after applying a data augmentation operation. for each instance x i , we find one distractor sentence from the document of the gold evidence that is the most similar to the claim by word overlap. we append the distractor sentence to the original evidence, which serves as a positive instance (figure 3 , left). finally, we include only the distractor sentence as a negative instance as it does not have enough evidence contrasted both with the positive and the anchor instances. we conjecture that the latter would serve as a training signal for avoiding the bias for overlap between the claim and the evidence. ## conclusion we propose a new task related to fact checking, namely, detecting when evidence with omitted information is (in)sufficient. to this end, we conducted an in-depth empirical analysis with a newly introduced fluency-preserving method for omitting evidence information. we compared what transformer-based models and humans find to be sufficient information for fc, resulting in a novel dataset, sufficientfacts. finally, we showed that the proposed evidence omission method can be used for collecting contrastive examples for cl and cad, which improved the performance of the studied models on the evidence sufficiency prediction task and on veracity prediction. the resulting models could be applied to detect emergent false claims, which gain popularity before any reputable source can refute them, as our proposed models can indicate when the provided input is insufficient for making a decision and whether to provide the user with the veracity prediction. such models could also be used for detecting knowledge or evidence gaps that need to be filled to refute or support popular claims. another possible future research direction would be to build fc models that indicate the particular part of the claim that they are missing supporting evidence for. moreover, our proposed analysis and methods could be applied to other knowledgeintensive tasks, such as question answering.