SlowGuess/ACL_2020_OCR huggingface dataset:
Pepa Atanasova Jakob Grue Simonsen Christina Lioma Isabelle Augenstein
{pepa, simonsen, c.lioma, augenstein}@di.ku.dk
Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process - generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model.
When a potentially viral news item is rapidly or indiscriminately published by a news outlet, the responsibility of verifying the truthfulness of the item is often passed on to the audience. To alleviate this problem, independent teams of professional fact checkers manually verify the veracity and credibility of common or particularly checkworthy statements circulating the web. However, these teams have limited resources to perform manual fact checks, thus creating a need for automating the fact checking process.
The current research landscape in automated fact checking is comprised of systems that estimate the veracity of claims based on available metadata and evidence pages. Datasets like LIAR (Wang, 2017) and the multi-domain dataset MultiFC (Augenstein et al., 2019) provide real-world
Claim: The last major oil spill from a drilling accident in America happened over 40 years ago in 1969.
Ruling Comments: (...) The last major oil spill from a drilling accident in America happened over 40 years ago in 1969.
(...) The largest in volume was the Santa Barbara spill of 1969 referenced by Murdock and Johnson, in which an estimated 100,000 barrels of oil spilled into the Pacific Ocean, according to the API. The Santa Barbara spill was so big it ranked seventh among the 10 largest oil spills caused by marine well blowouts in the world, the report states. Two other U.S. spills, both in 1970, rank eighth and 10th. Fourteen marine blowouts have taken place in the U.S. between 1969 and 2007. Six of them took place after 1990 and spilled a total of nearly 13,700 barrels.
(...) We interviewed three scientists who said that the impact of a spill has little to do with its volume. Scientists have proven that spills far smaller than Santa Barbara's have been devastating.
Justification: While the nation's largest oil well blowout did take place in 1969, it's not factually correct to call it the "last major oil spill". First of all, two of the largest blowouts in the world took place in the U. S. the following year. More importantly, experts agree that spills far smaller in volume to the 1969 disaster have been devastating. From a scientific perspective, Johnson's decision to single out the 1969 blowout as the last "major" one makes no sense.
Ruling: Half-True
Table 1: Example instance from the LIAR-PLUS dataset, with oracle sentences for generating the justification highlighted.
benchmarks for evaluation. There are also artificial datasets of a larger scale, e.g., the FEVER (Thorne et al., 2018) dataset based on Wikipedia articles. As evident from the effectiveness of state-of-the-art methods for both real-world – 0.492 macro F1 score (Augenstein et al., 2019), and artificial data – 68.46 FEVER score (label accuracy conditioned on evidence provided for ‘supported’ and ‘refuted’ claims) (Stammbach and Neumann, 2019), the task of automating fact checking remains a significant and poignant research challenge.
A prevalent component of existing fact checking systems is a stance detection or textual entailment model that predicts whether a piece of evidence
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contradicts or supports a claim (Ma et al., 2018; Mohtarami et al., 2018; Xu et al., 2018). Existing research, however, rarely attempts to directly optimise the selection of relevant evidence, i.e., the self-sufficient explanation for predicting the veracity label (Thorne et al., 2018; Stammbach and Neumann, 2019). On the other hand, Alhindi et al. (2018) have reported a significant performance improvement of over  $10\%$  macro F1 score when the system is provided with a short human explanation of the veracity label. Still, there are no attempts at automatically producing explanations, and automating the most elaborate part of the process - producing the justification for the veracity prediction - is an understudied problem.
In the field of NLP as a whole, both explainability and interpretability methods have gained importance recently, because most state-of-the-art models are large, neural black-box models. Interpretability, on one hand, provides an overview of the inner workings of a trained model such that a user could, in principle, follow the same reasoning to come up with predictions for new instances. However, with the increasing number of neural units in published state-of-the-art models, it becomes infeasible for users to track all decisions being made by the models. Explainability, on the other hand, deals with providing local explanations about single data points that suggest the most salient areas from the input or are generated textual explanations for a particular prediction.
Saliency explanations have been studied extensively (Adebayo et al., 2018; Arras et al., 2019; Poerner et al., 2018), however, they only uncover regions with high contributions for the final prediction, while the reasoning process still remains behind the scenes. An alternative method explored in this paper is to generate textual explanations. In one of the few prior studies on this, the authors find that feeding generated explanations about multiple choice question answers to the answer predicting system improved QA performance (Rajani et al., 2019).
Inspired by this, we research how to generate explanations for veracity prediction. We frame this as a summarisation task, where, provided with elaborate fact checking reports, later referred to as ruling comments, the model has to generate veracity explanations close to the human justifications as in the example in Table 1. We then explore the benefits of training a joint model that learns to generate
veracity explanations while also predicting the ver- racity of a claim.
1. We present the first study on generating veracity explanations, showing that they can successfully describe the reasons behind a veracity prediction.
2. We find that the performance of a veracity classification system can leverage information from the elaborate ruling comments, and can be further improved by training veracity prediction and veracity explanation jointly.
3. We show that optimising the joint objective of veracity prediction and veracity explanation produces explanations that achieve better coverage and overall quality and serve better at explaining the correct veracity label than explanations learned solely to mimic human justifications.
Existing fact checking websites publish claim veracity verdicts along with ruling comments to support the verdicts. Most ruling comments span over long pages and contain redundancies, making them hard to follow. Textual explanations, by contrast, are succinct and provide the main arguments behind the decision. PolitiFact<sup>1</sup> provides a summary of a claim's ruling comments that summarises the whole explanation in just a few sentences.
We use the PolitiFact-based dataset LIAR-PLUS (Alhindi et al., 2018), which contains 12,836 statements with their veracity justifications. The justifications are automatically extracted from the long ruling comments, as their location is clearly indicated at the end of the ruling comments. Any sentences with words indicating the label, which Alhindi et al. (2018) select to be identical or similar to the label, are removed. We follow the same procedure to also extract the ruling comments without the summary at hand.
We remove instances that contain fewer than three sentences in the ruling comments as they indicate short veracity reports, where no summary is present. The final dataset consists of 10,146 training, 1,278 validation, and 1,255 test data points. A claim's ruling comments in the dataset span over 39 sentences or 904 words on average, while the justification fits in four sentences or 89 words on average.
<sup>1</sup>https://www.politifact.com/
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We now describe the models we employ for training separately (1) an explanation extraction and (2) veracity prediction, as well as (3) the joint model trained to optimise both.
The models are based on DistilBERT (Sanh et al., 2019), which is a reduced version of BERT (Devlin et al., 2019) performing on par with it as reported by the authors. For each of the models described below, we take the version of DistilBERT that is pre-trained with a language-modelling objective and further fine-tune its embeddings for the specific task at hand.
3.1 Generating Explanations
Our explanation model, shown in Figure 1 (left) is inspired by the recent success of utilising the transformer model architecture for extractive summarisation (Liu and Lapata, 2019). It learns to maximize the similarity of the extracted explanation with the human justification.
We start by greedily selecting the top  $k$  sentences from each claim's ruling comments that achieve the highest ROUGE-2 F1 score when compared to the gold justification. We choose  $k = 4$ , as that is the average number of sentences in veracity justifications. The selected sentences, referred to as oracles, serve as positive gold labels -  $\mathbf{y}^E \in \{0,1\}^N$ , where  $N$  is the total number of sentences present in the ruling comments. Appendix A.1 provides an overview of the coverage that the extracted oracles achieve compared to the gold justification. Appendix A.2 further presents examples of the selected oracles, compared to the gold justification.
At training time, we learn a function  $f(X) = \mathbf{p}^{E}, \mathbf{p}^{E} \in \mathbb{R}^{1,N}$  that, based on the input  $X$ , the text of the claim and the ruling comments, predicts which sentence should be selected -  $\{0,1\}$ , to constitute the explanation. At inference time, we select the top  $n = 4$  sentences with the highest confidence scores.
Our extraction model, represented by function  $f(X)$ , takes the contextual representations produced by the last layer of DistilBERT and feeds them into a feed-forward task-specific layer -  $\mathbf{h} \in \mathbb{R}^h$ . It is followed by the prediction layer  $\mathbf{p}^E \in \mathbb{R}^{1,N}$  with sigmoid activation. The prediction is used to optimise the cross-entropy loss function  $\mathcal{L}_E = \mathcal{H}(\mathbf{p}^E, \mathbf{y}^E)$ .
3.2 Veracity Prediction
For the veracity prediction model, shown in Figure 1 (right), we learn a function  $g(X) = \mathbf{p}^{F}$  that, based on the input X, predicts the veracity of the claim  $\mathbf{y}^{F} \in Y_{F}, Y_{F} = \{\text{true, false, half-true, barely-true, mostly-true, pants-on-fire}\}$ .
The function  $g(X)$  takes the contextual token representations from the last layer of DistilBERT and feeds them to a task-specific feed-forward layer  $\mathbf{h} \in \mathbb{R}^h$ . It is followed by the prediction layer with a softmax activation  $\mathbf{p}^F \in \mathbb{R}^6$ . We use the prediction to optimise a cross-entropy loss function  $\mathcal{L}_F = \mathcal{H}(\mathbf{p}^F, \mathbf{y}^F)$ .
3.3 Joint Training
Finally, we learn a function  $h(X) = (\mathbf{p}^E, \mathbf{p}^F)$  that, given the input  $X$  - the text of the claim and the ruling comments, predicts both the veracity explanation  $\mathbf{p}^E$  and the veracity label  $\mathbf{p}^F$  of a claim. The model is shown in Figure 2. The function  $h(X)$  takes the contextual embeddings  $\mathbf{c}^E$  and  $\mathbf{c}^F$  produced by the last layer of DistilBERT and feeds them into a cross-stitch layer (Misra et al., 2016; Ruder et al., 2019), which consists of two layers with two shared subspaces each -  $\mathbf{h}_E^1$  and  $\mathbf{h}_E^2$  for the explanation task and  $\mathbf{h}_F^1$  and  $\mathbf{h}_F^2$  for the veracity prediction task. In each of the two layers, there is one subspace for task-specific representations and one that learns cross-task representations. The subspaces and layers interact through  $\alpha$  values, creating the linear combinations  $\widetilde{h}_E^i$  and  $\widetilde{h}_F^j$ , where  $i,j \in \{1,2\}$ :
$$
\left[ \begin{array}{l} \widetilde {h} _ {E} ^ {i} \\ \widetilde {h} _ {F} ^ {j} \end{array} \right] = \left[ \begin{array}{l l} \alpha_ {E E} & \alpha_ {E F} \\ \alpha_ {F E} & \alpha_ {F F} \end{array} \right] \left[ \begin{array}{l l} h _ {E} ^ {i T} & h _ {F} ^ {j T} \end{array} \right] \tag {1}
$$
We further combine the resulting two subspaces for each task -  $\widetilde{h}_E^i$  and  $\widetilde{h}_F^j$  with parameters  $\beta$  to produce one representation per task:
$$
\widetilde {h} _ {P} ^ {T} = \left[ \begin{array}{l} \beta_ {P} ^ {1} \\ \beta_ {P} ^ {2} \end{array} \right] ^ {T} \left[ \begin{array}{l l} \widetilde {h} _ {P} ^ {1} & \widetilde {h} _ {P} ^ {2} \end{array} \right] ^ {T} \tag {2}
$$
where  $\mathbf{P}\in \{E,F\}$  is the corresponding task.
Finally, we use the produced representation to predict  $\mathbf{p}^E$  and  $\mathbf{p}^F$ , with feed-forward layers followed by sigmoid and softmax activations accordingly. We use the prediction to optimise the joint loss function  $\mathcal{L}_{MT} = \gamma * \mathcal{H}(\mathbf{p}^E, \mathbf{y}^E) + \eta * \mathcal{H}(\mathbf{p}^F, \mathbf{y}^F)$ , where  $\gamma$  and  $\eta$  are used for weighted combination of the individual loss functions.
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Figure 1: Architecture of the Explanation (left) and Fact-Checking (right) models that optimise separate objectives.
Figure 2: Architecture of the Joint model learning Explanation (E) and Fact-Checking (F) at the same time.
4 Automatic Evaluation
We first conduct an automatic evaluation of both the veracity prediction and veracity explanation models.
In Table 3, we compare the performance of the two proposed models for generating extractive explanations. Explain-MT is trained jointly with a veracity prediction model, and Explain-Extractive is trained separately. We include the Lead-4 system (Nallapati et al., 2017) as a baseline, which selects as a summary the first four sentences from the ruling comments. The Oracle system presents the best greedy approximation of the justification with sentences extracted from the ruling comments. It indicates the upper bound that could be achieved by extracting sentences from the ruling comments as an explanation. The performance of the models is measured using ROUGE-1, ROUGE-2, and
ROUGE-L F1 scores.
In Table 2, we again compare two models - one trained jointly - MT-Veracity@Rul, with the explanation generation task and one trained separately - Veracity@Rul. As a baseline, we report the work of Wang (2017), who train a model based on the metadata available about the claim. It is the best known model that uses only the information available from the LIAR dataset and not the gold justification, which we aim at generating.
We also provide two upper bounds serving as an indication of the approximate best performance that can be achieved given the gold justification. The first is the reported system performance from Alhindi et al. (2018), and the second - Veracity@Just, is our veracity prediction model but trained on gold justifications. The Alhindi et al. (2018) system is trained using a BiLSTM, while we train the Veracity@Just model using the same model architecture as for predicting the veracity from the ruling comments with Veracity@Rul.
Lastly, Veracity@RulOracles is the veracity model trained on the gold oracle sentences from the ruling comments. It provides a rough estimate of how much of the important information from the ruling comments is preserved in the oracles. The models are evaluated with a macro F1 score.
Our models employ the base, uncased version of the pre-trained DistilBERT model. The models are fed with text depending on the task set-up - claim and ruling sentences for the explanation and joint models; claim and ruling sentences, claim and oracle sentences or claim and justification for the fact-checking model. We insert a '[CLS]' token before the start of each ruling sentence (explanation model), before the claim (fact-checking model), or
at the combination of both for the joint model. The text sequence is passed through a number of Transformer layers from DistilBERT. We use the '[CLS]' embeddings from the final contextual layer of DistilBERT and feed that in task-specific feed-forward layers  $\mathbf{h} \in \mathbb{R}^h$ , where  $\mathbf{h}$  is 100 for the explanation task, 150 for the veracity prediction one and 100 for each of the joint cross-stitch subspaces. Following are the task-specific prediction layers  $p^E$ .
The size of  $h$  is picked with grid-search over  $\{50, 100, 150, 200, 300\}$ . We also experimented with replacing the feed-forward task-specific layers with an RNN or Transformer layer or including an activation function, which did not improve task performance.
The models are trained for up to 3 epochs, and, following Liu and Lapata (2019), we evaluate the performance of the fine-tuned model on the validation set at every 50 steps, after the first epoch. We then select the model with the best ROUGE-2 F1 score on the validation set, thus, performing a potential early stopping. The learning rate used is 3e-5, which is chosen with a grid search over \{3e-5, 4e-5, 5e-5\}. We perform 175 warm-up steps (5% of the total number of steps), after also experimenting with 0, 100, and 1000 warm-up steps. Optimisation is performed with AdamW (Loshchilov and Hutter, 2017), and the learning rate is scheduled with a warm-up linear schedule (Goyal et al., 2017). The batch size during training and evaluation is 8.
The maximum input words to DistilBERT are 512, while the average length of the ruling comments is 904 words. To prevent the loss of any sentences from the ruling comments, we apply a sliding window over the input of the text and then merge the contextual representations of the separate sliding windows, mean averaging the representations in the overlap of the windows. The size of the sliding window is 300, with a stride of 60 tokens, which is the number of overlapping tokens between two successive windows. The maximum length of the encoded sequence is 1200. We find that these hyper-parameters have the best performance after experimenting with different values in a grid search.
We also include a dropout layer (with 0.1 rate for the separate and 0.15 for the joint model) after the contextual embedding provided by the transformer models and after the first linear layer as well.
The models optimise cross-entropy loss, and the joint model optimises a weighted combination of
Model	Val	Test
Wang (2017), all metadata	0.247	0.274
Veracity@RulOracles	0.308	0.300
Veracity@Rul	0.313	0.313
MT-Veracity@Rul	0.321	0.323
Alhindi et al. (2018)@Just	0.37	0.37
Veracity@Just	0.443	0.443
Table 2: Results (Macro F1 scores) of the veracity prediction task on all of the six classes. The models are trained using the text from the ruling oracles (@RulOracles), ruling comment (@Rul), or the gold justification (@Just).
both losses. Weights are selected with a grid search - 0.9 for the task of explanation generation and 0.1 for veracity prediction. The best performance is reached with weights that bring the losses of the individual models to roughly the same scale.
For each claim, our proposed joint model (see 3.3) provides both (i) a veracity explanation and (ii) a veracity prediction. We compare our model's performance with models that learn to optimise these objectives separately, as no other joint models have been proposed. Table 2 shows the results of veracity prediction, measured in terms of macro F1.
Judging from the performance of both Veracity@Rul and MT-Veracity@Rul, we can assume that the task is very challenging. Even given a gold explanation (Alhindi et al. (2018) and Veracity@Just), the macro F1 remains below 0.5. This can be due to the small size of the dataset and/or the difficulty of the task even for human annotators. We further investigate the difficulty of the task in a human evaluation, presented in Section 5.
Comparing Veracity@RulOracles and Veracity@Rul, the latter achieves a slightly higher macro F1 score, indicating that the extracted ruling oracles, while approximating the gold justification, omit information that is important for veracity prediction. Finally, when the fact checking system is learned jointly with the veracity explanation system - MT-Veracity@Rul, it achieves the best macro F1 score of the three systems. The objective to extract explanations provides information about regions in the ruling comments that are close to the gold explanation, which helps the veracity prediction model to choose the correct piece of evidence.
In Table 3, we present an evaluation of the generated explanations, computing ROUGE F1 score
Model	Validation	Test
ROUGE-1	ROUGE-2	ROUGE-L	ROUGE-1	ROUGE-2	ROUGE-L
Lead-4	27.92	6.94	24.26	28.11	6.96	24.38
Oracle	43.27	22.01	38.89	43.57	22.23	39.26
Explain-Extractive	35.64	13.50	31.44	35.70	13.51	31.58
Explain-MT	35.18	12.94	30.95	35.13	12.90	30.93
Table 3: Results of the veracity explanation generation task. The results are ROUGE-N F1 scores of the generated explanation w.r.t. the gold justification.
w.r.t. gold justification. Our first model, the Explain-Extractive system, optimises the single objective of selecting explanation sentences. It outperforms the baseline, indicating that generating veracity explanations is possible.
Explain-Extractive also outperforms the Explain-MT system. While we would expect that training jointly with a veracity prediction objective would improve the performance of the explanation model, as it does for the veracity prediction model, we observe the opposite. This indicates a potential mismatch between the ruling oracles and the salient regions for the fact checking model. We also find a potential indication of that in the observed performance decrease when the veracity model is trained solely on the ruling oracles compared to the one trained on all of the ruling comments. We hypothesise that, when trained jointly with the veracity extraction component, the explanation model starts to also take into account the actual knowledge needed to perform the fact check, which might not match the exact wording present in the oracles, thus decreasing the overall performance of the explanation system. We further investigate this in a manual evaluation of which of the systems - Explain-MT and Explain-Extractive, generates explanations with better qualities and with more information about the veracity label.
Finally, comparing the performance of the extractive models and the Oracle, we can conclude that there is still room for improvement of explanation systems when only considering extractive summarisation.
4.4 A Case Study
Table 4 presents two example explanations generated by the extractive vs. the multi-task model. In the first example, the multi-task explanation achieves higher ROUGE scores than the extractive one. The corresponding extractive summary contains information that is not important for the final veracity label, which also appears to affect the
ROUGE scores of the explanation. On the other hand, the multi-task model, trained jointly with a veracity prediction component, selects sentences that are more important for the fact check, which in this case is also beneficial for the final ROUGE score of the explanation.