Evidence-based Factual Error Correction (2021)

Abstract: This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. This extends the well-studied task of fact verification by providing a mechanism to correct written texts that are refuted or only partially supported by evidence. We demonstrate that it is feasible to train factual error correction systems from existing fact checking datasets which only contain labeled claims accompanied by evidence, but not the correction. We achieve this by employing a two-stage distant supervision approach that incorporates evidence into masked claims when generating corrections. Our approach, based on the T5 transformer and using retrieved evidence, achieved better results than existing work which used a pointer copy network and gold evidence, producing accurate factual error corrections for 5x more instances in human evaluation and a .125 increase in SARI score. The evaluation is conducted on a dataset of 65,000 instances based on a recent fact verification shared task and we release it to enable further work on the task.

## introduction fact verification is the task of predicting whether claims are true or false using evidence. with the availability of a number of resources @xcite @xcite @xcite , the task has attracted significant attention and spawned the development of new models, architectures and approaches. with potentially sensitive applications, recent works have focused on building explainable variants of fact checking @xcite @xcite . exposing the evidence source and 1 https://github.com/j6mes/ 2021-acl-factual-error-correction ## related work a number of related works offer methods to make corrections to sentences. however, their use of external information differs. this can be placed on a continuum from only using the knowledge captured during language model pre-training, to conditioning generation based on a context sentence. we briefly outline key methods and approaches below. grammatical error correction (gec) @xcite @xcite is the task of making meaning-preserving changes to sentences such that grammatical errors made by language learners are removed. no external information is required as the sentence is undergoing a surface-level transformation where the (intended) semantic content of the sentence should remain unchanged. in contrast, the semantic content of sentences undergoing factual error correction will be altered, if needed, to better align the meaning with ground truth evidence. shah et al. (2020) make meaningaltering updates to sentences in wikipedia in a two step process that does not require reference corrections in training: salient tokens are masked and a corrector conditionally replaces the masks with ground truth evidence. in this approach, token salience is predicted by querying a model that is trained to perform fact verification for a claim against evidence. @xcite generate corrections as a post-editing step for outputs from abstractive summarization so that they are consistent with the source text. their approach uses a sequence-tosequence model trained to restore artificially generated corruptions of a reference summary. one potential way to introduce knowledge is to use information stored in the parameters of largescale pre-trained language models @xcite . the language model can be used recover tokens responsible for causing factual errors that are masked out as a variant of cloze-style evaluation @xcite . while such approaches have been employed for fact verification @xcite , these approaches share the following limitations. without explicit control @xcite , the most likely token when decoded may not be factually accurate, or supported by the retrieved evidence, commonly referred to as a hallucination @xcite . furthermore, even if the information stored within language model parameters could be reliably retrieved for factual error correction, facts change over time and the need to obtain information from up-to-date sources becomes greater as the state of the world diverges from the information captured within the model parameters. recent language models augmented with a retrieval component such as realm @xcite and rag @xcite ) could be applied, however, task-specific fine-tuning would still be required to condition the generation based on the factual error to mitigate hallucination. r1 -intelligible similar to other language generation tasks, our first requirement is that generated outputs are fluent and intelligible. they must be free of grammatical mistakes and the meaning must be understandable without the aid of additional context or evidence so that their factual correctness can be assessed. ## task decomposition the choice of supervision for the error correction system influences the task decomposition. for example, with full supervision, the system can be constructed with an information retrieval module and a sequence-to-sequence module that conditionally generates a correction given the claim and evidence. however, large datasets of claims paired with corrections are not available. the absence of full supervision requires that we distantly-supervise our systems using fact verification datasets, which are an abundant resource. fact verification datasets contain claims labeled with evidence but do not contain corrections. with this resource, we propose a task decomposition that generated corrections by training models to reconstruct claims with masked tokens using retrieved evidence. ## data we make use of fever @xcite , a commonly used fact verification dataset, as the basis for our experiments. fever is one of the largest resources consisting of claims paired with evidence from wikipedia. there are 185k instances with corresponding evidence sentences and a label as to whether the claim is supported or re-futed by it. claims where no information could be found are labeled as notenoughinfo. to comprehensively evaluate the corrections generated manual evaluation is required. however, this is expensive and not suitable for system development and hyper-parameter optimization. to automate system evaluation or to train a seq2seq model with full supervision, a reference "gold standard" correction is also required. for this, we release annotations from the fever shared task as follows. the claims in fever were generated in a two-stage process: annotators extracted facts from wikipedia and then performed meaning altering perturbations called mutations over these extracted facts. each claim was independently labeled using retrieved evidence. our reference corrections are the unmodified facts extracted from wikipedia. the class balance and size of the dataset is re-ported in ## evaluation while it's convenient to use an automatic metric during development, these metrics compute token overlap against a single reference sentence and cannot capture the nuances required to assess the veracity of the generated corrections against evidence. thus, our primary evaluation will use human raters to label whether the model predictions meet the task requirements stated in section 3. human raters are asked three questions about system outputs to assess whether the corrections meet the requirements of intelligibility, supported by evidence, and error correction introduced in section 3. for the first 2 requirements, the question has a binary answer. for the third requirement of error correction, the question has 3 answer choices: for automated evaluation, we use sari @xcite which is a metric used for sentence simplification. sari considers ngrams retained from the source as well added or deleted ngrams through comparison against a reference sentence. we additionally report bleu @xcite and rouge @xcite to indicate precision and recall of the correction. in section 9, we report correlation of automated metrics against our manual evaluation. fully supervised ceiling we use this model to estimate the ceiling performance of a factual error correction system (assuming a reasonable amount of training data is available) that other methods can be compared against. we fine-tune a t5-base model with supervision of the correction (see section 6), using the same hyper-parameter choices as the t5 masker-corrector. automated scoring a single reference sentence from the fever dataset is used for automated scoring. we consider bleu, rouge, and sari. sari considers the f1 of added tokens, f1 of kept tokens, precision of deletions, and the mean of these 3 scores (denoted final). we use code made available by @xcite . evidence retrieval we use the facebook implementation of dpr @xcite without fine-tuning and constructed an index over the wikipedia version released with fever @xcite , chunked into passages of 50 tokens. for genre, the original authors' implementation was used. we selected the top matching 2 passages. this resulted in the highest scores on the downstream corrections; sari was lower when using 1 or 3 passages. maskers for the white-box masker, we use the implementation provided by @xcite applied to our dataset retaining original hyperparameters trained on fever. for the black-box masker, we use the lime implementation from @xcite to probe a bert classifier @xcite fine-tuned on fever. for the lm and random baseline maskers, where the number of masks was tunable, we masked 50% of the tokens, which was similar to the number of tokens masked by the black-and white-box maskers. language model as correctors? we greedily decode masked tokens using a bert-base-cased language model using the huggingface implementation @xcite without fine-tuning. comparison to previous work for comparison to previous work, we use the dual-encoder pointer network implementation from @xcite , retaining the original hyper-parameter choices. ## results we first report results from a manual evaluation, assessing the requirements that corrections are intelligible, supported by evidence, and improve the factuality of the claim, as listed in section 3. our evaluation considers a sample of 200 instances per system. we report the results in the fully supervised models had the highest rate of satisfactory corrections that improved the factuality of the claim (requirement 3), indicating a performance ceiling for the distantly-supervised models. incorporating retrieved evidence in these supervised models (rather than gold) reduced the number of corrections supported by evidence from 88.9% to 64.7% and the number of satisfactory corrections from 68.9% to 48.9% showing the challenges of incorporating (possibly noisy) retrieved evidence when generating the corrections. when using the masker and corrector distant supervision strategy, different maskers could be used to train the corrector to the masker used at test time. we observed that training the corrector with random masks yielded both a higher rate of satisfactory corrections and corrections supported by evidence when using either the black-box or heuristic masker at test time. we further evaluate other maskers with automated metrics in section 9.2. using a heuristic masker at test time, which removed tokens from the claim not present in the evidence, generated more claims meeting the supported and corrected requirements than masks generated by querying a fact verification model (both black-box and white-box). an analysis of the masker's influence on the corrections is provided in section 9.1. the two baseline systems, dual encoder m+c, based on shah et al. (2020), and a pre-trained bert language model, generated corrections that were intelligible or supported by evidence at a lower rate than the aforementioned models, further discussed in sections 9.3 and 9.4. we report the correlation between automated scoring metrics and our manual evaluation in ## qualitative error analysis in this section we discuss the following issues which were present in all master-corrector systems: over-erasure in some instances, the masker removed most or all of the non-stopword tokens from the claim. this resulted in the original meaning of the claim being erased. without this information the corrector could not reconstruct the claim, resulting in corrections that were unrelated to the input claim. this issue was most prevalent for the blackbox masker, where 15% of instances had more than 5 consecutive tokens masked and 32% of instances had 4 consecutive tokens masked. in contrast, the heuristic masker, which identifies the tokens not present in the retrieved evidence had 5 consecutive tokens masked for 3% of instances and 4 consecutive tokens masked for 9% of instances. while, in some cases, appropriate corrections could be made despite the aggressive masking (e.g. the claim "exit the king is by man @xcite ." was fully masked, but corrected to include the author's name), others were re-written focusing on a different fact, e.g. a claim about the length of reign of maria theresa was rewritten to be about her date of birth. incorrect masking when the erroneous tokens in a claim were not masked, the corrector would generate outputs not supported by evidence. for example the following claim, which has an incorrect year, was masked but retaining the error: "ghost, the film was released in 1994" as "[mask] , [mask] [mask] [mask] [mask] [mask] in 1994". even with suitable retrieved evidence, indicating the release year is 1990, no appropriate correction could be made. ## conclusions and future work going beyond simply identifying errors, factual error correction presents a number of challenges for information retrieval, fact verification and abstractive summarization communities alike. in this paper, we demonstrated that the task can be performed with distant supervision in the form of claims labeled by evidence supporting or refuting them. however, there are a number of outstanding challenges that must be addressed. the data we used from the fever task was re-purposed to evaluate whether systems can undo mutations introduced by human annotators and may not be representative of the range of factual errors that would be present in real-world documents. while some automated metrics correlated well with human judgements, future work should consider how automated scoring can be better used to discriminate the adequacy of the generated corrections going beyond similarity to the reference sentence. from a modelling perspective, the masks strongly influenced the corrector and further work is required to generate masks that result in better corrections. we observed where masks mismatched the evidence, the correction was vague, hallucinated or did not correct the factual errors in the claim. this could be addressed through joint training of both components to enable them to avoid error propagation from masking to correction.