Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction
Year: 2023

Abstract:
We present Claim-Dissector: a novel latent variable model for fact-checking and analysis, which given a claim and a set of retrieved evidences jointly learns to identify: (i) the relevant evidences to the given claim, (ii) the veracity of the claim. We propose to disentangle the per-evidence relevance probability and its contribution to the final veracity probability in an interpretable way — the final veracity probability is proportional to a linear ensemble of per-evidence relevance probabilities. In this way, the individual contributions of evidences towards the final predicted probability can be identified. In per-evidence relevance probability, our model can further distinguish whether each relevant evidence is supporting (S) or refuting (R) the claim. This allows to quantify how much the S/R probability contributes to the final verdict or to detect disagreeing evidence. Despite its interpretable nature, our system achieves results competitive with state-of-theart on the FEVER dataset, as compared to typical two-stage system pipelines, while using significantly fewer parameters. It also sets new state-of-the-art on FAVIQ and RealFC datasets. Furthermore, our analysis shows that our model can learn fine-grained relevance cues while using coarse-grained supervision, and we demonstrate it in 2 ways. (i) We show that our model can achieve competitive sentence recall while using only paragraph-level relevance supervision. (ii) Traversing towards the finest granularity of relevance, we show that our model is capable of identifying relevance at the token level. To do this, we present a new benchmark TLRFEVER focusing on token-level interpretability — humans annotate tokens in relevant evidences they considered essential when making their judgment. Then we measure how similar are these annotations to the tokens our model is focusing on.1 https://github.com/KNOT-FIT-BUT/ClaimDissector.

heading: 1 Introduction
text: Today’s automated fact-checking systems are moving from predicting the claim’s veracity by capturing the superficial cues of credibility, such as the way the claim is written, the statistics captured in the claim author’s profile, or the stances of its respondents on social networks (Zubiaga et al., 2016; Derczynski et al., 2017; Gorrell et al., 2019; Fajcik et al., 2019; Li et al., 2019) towards evidencegrounded systems which, given a claim, identify relevant sources and then use these to predict the claim’s veracity (Thorne et al., 2018; Jiang et al., 2020; Park et al., 2022). In practice, providing precise evidence turns out to be at least as important as predicting the veracity itself. Disproving a claim without linking it to factual evidence often fails to be persuasive and can even cause a “backfire” effect — refreshing and strengthening the belief into an erroneous claim (Lewandowsky et al., 2012)2.
For evidence-grounded fact-checking, most of the existing state-of-the-art systems (Jiang et al., 2021; Stammbach, 2021; Khattab et al., 2021) employ a 3-stage cascade approach; given a claim, they retrieve relevant documents, rerank relevant evidences (sentences, paragraphs or larger text blocks) within these documents, and predict the claim’s veracity from the top-k (usually k=5) relevant evidences.
This comes with several drawbacks; firstly, the multiple steps of the system lead to error propagation, i.e. the input to the last system might often be too noisy to contain any information. Some previous work focused on merging evidence reranking and veracity prediction into a single step (Ma et al., 2019; Schlichtkrull et al., 2021). Secondly, in opendomain setting, number of relevant evidences can be significantly larger than k3, especially when there is a lot of repeated evidence. Thirdly, in open-
2Further discussion in Appendix K. 3e.g.,~3.7 % of FEVER’s non-exhaustive annotations.
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domain setting, sometimes there is both, supporting and refuting evidence. The re-ranking systems often do not distinguish whether evidence is relevant because it supports or refutes the claim, and thus may select the evidence from one group based on the in-built biases.
To further strengthen the persuasive effect of the evidences and understand the model’s reasoning process, some of these systems provide cues of interpretability (Popat et al., 2018; Liu et al., 2020). However, the interpretability in the mentioned work was often considered a useful trait, which was evaluated only qualitatively, as the laborintensive human evaluation was out of the scope of their focus.
To this extent, we propose Claim-Dissector (CD), a latent variable model which:
1. jointly ranks top-relevant, top-supporting and top-refuting evidences, and predicts veracity of the claim in an interpretable way, where the probability of the claim’s veracity is estimated using the linear combination of per-evidence probabilities (Subsection 2.2),
2. can provide fine-grained (sentence-level or token-level evidence), while using only coarse-grained supervision (on block-level or sentence-level respectively),
3. can be parametrized from a spectrum of language representation models (such as RoBERTa or DeBERTaV3 (Liu et al., 2019; He et al., 2021)).
Finally, we collect a 4-way annotated dataset TLR-FEVER of per-token relevance annotations. This serves as a proxy for evaluating interpretability: we measure how similar are the cues provided by the model to the ones from humans. We believe future work can benefit from our quantitative evaluation approach while maintaining focus.

heading: 2 Model Description
text: We present a 2-stage system composed of the retriever and the verifier. The documents are ranked via retriever. Each document is split into blocks. The blocks from top-ranking documents are passed to verifier and jointly judged. Our interpretable CD verifier is capable of re-ranking documents for any granularity of relevant evidence (e.g., document, block, sentence, token). Jointly, the same model predicts the claim’s veracity. The overall schema of our approach is depicted in Figure 1.

heading: 2.1 Retriever
text: Given a claim c ∈ C from the set of all possible claims C and the corpus D = {d1, d2, ..., dn} composed of documents di, the retriever produces a ranking using function rank : C × D → R that assigns a claim-dependent score to each document in the corpus. In this work, we focus on the verifier; therefore, we take the strong retriever from Jiang et al. (2021). This retriever interleaves documents ranked by BM25 (Robertson and Zaragoza, 2009) (a1, a2, ...an) and Wikipedia API (b1, b2, ...bm) following Hanselowski et al. (2018) as (a1, b1, a2, b2, ...), while skipping duplicate articles. Each document is then split into nonoverlapping blocks of size Lx, respecting sentence boundaries4. Our verifier then computes its veracity prediction from top-K1 such blocks. To keep up with similar approaches (Hanselowski et al., 2018; Stammbach and Neumann, 2019), we also experiment with expanding evidence with documents hyperlinked to the top retrieved articles. We rank these documents according to rank and sequential order in the document they were hyperlinked from. We then process these extra ranked documents the same way as retrieved documents, adding top-K2 blocks to the verifier’s input. As discussed more
4Every block contains as many sentences as can fit into Lx tokens, considering verifier’s tokenization.
closely in Stammbach and Neumann (2019), some relevant documents are impossible to retrieve using just claim itself, as their relevance is conditioned on other relevant documents. However, we stress that such approaches also mimic the way FEVER dataset was collected, and thus the improvements of such approach on “naturally collected” datasets might be negligible if any.

heading: 2.2 Verifier
text: The verifier first processes each block independently using a language representation model (LRM) and then aggregates cross-block information via multi-head attention (Vaswani et al., 2017), computing matrix M . This matrix is used to compute both, the probability of each evidence’s relevance and the probability of the claim’s veracity. Furthermore, the way the model is constructed allows learning a linear relationship between these probability spaces.
Formally given a claim c and K = K1 + K2 blocks, K input sequences xi for each block i are constructed as
[CLS] <claim> c [SEP] <title> t
<passage> s1 <sentence> s2 <sentence>...s#<sentence> [SEP],
where [CLS] and [SEP] are transformer special tokens used during the LRM pre-training (Devlin et al., 2019). Each block is paired with its article’s title t and split into sentences s1, s2, ..., s#. Symbols c, t, s1, s2, ..., s# thus each denote a sequence of tokens. We further introduce new special tokens <claim>, <title>, <passage>, <sentence> to separate different input parts. Crucially, every sentence is appended with a <sentence> token. Their respective embeddings are trained from scratch. Each input xi is then encoded via LRM Ei = LRM(xi) ∈ RLB×d, where LB is an input sequence length, and d is LRM’s hidden dimensionality. The representations of every block are then concatenated into E = [E1;E2; ...;EK ] ∈ RL×d, where L is the number of all tokens in the input sequences from all retrieved blocks. Then we index-select all representations from E corresponding to positions of sentence tokens in s1, s2, ..., s# into score matrix Es ∈ RLe×d, where Le corresponds to the number of all tokens in all input sentences (without special tokens). Similarly, we index-select all representations at the same positions as the special <sentence> tokens at the input from E into
matrix S ∈ RLS×d, where LS ≪ Le is the total number of sentences in all inputs xi. The matrix M ∈ RLe×3 is then given as
M = SLP(MHAtt(Es,S,S))W . (1)
The MHAtt : . . . → RLe×d operator is a multihead attention with queries Es, and keys and values S. The SLP operator is a single layer perceptron with layer norm, dropout, and GeLU activation (details in Appendix G) and W ∈ Rd×3 is a linear transformation, projecting resulting vectors to the desired number of classes (3 in case of FEVER).
To compute the per-evidence probabilities we split the matrix M according to tokens belonging to each evidence. For instance, for sentencelevel evidence granularity we do split M = [M s1,1;M s2,1; ...;M s#,K ] along dimension Le into submatrix representations corresponding to sentence s1 in block 1 up to last sentence s# in block K. We then independently normalize each such matrix of i-th evidence of j-th block as5:
Pi,j(w, y) = expM i,jw,y∑
w′ ∑ y′ expM i,j w′,y′ . (2)
Note that w ∈ {1, 2, ..., |si,j |} is a token index in the (i,j)-th evidence and y ∈ {S, R, NEI} is the class label. Then we marginalize over latent variable w to obtain the marginal log-probability per evidence.
log Pi,j(y) = log ∑
w′ Pi,j(y, w′) (3)
Then objective LR is computed for evidences annotated in label set A = {(y∗1, (i1, j1)), ...} (usually |A| ≪ LS) for a single claim6.
LR = 1 |A| ∑
y∗,(i,j)∈A log Pi,j(y∗) (4)
In training, A contains the same amount of relevant and irrelevant labels. For relevant, the logprobability log Pi,j(y = y∗) is maximized, based the overall claim’s veracity label y∗ ∈ {S,R}. For irrelevant evidences, y∗ = IRR is maximized. As FEVER contains only annotation of relevant sentences, we follow the heuristic of Jiang et al.
5Note that the probability also depends on input sequences {xi}i∈{1,2,...,K}, but we omit this dependency for brevity.
6If example has NEI veracity in FEVER, LR = 0.
(2021) and sample irrelevant sentences ranked between 50 and 200, in order to avoid maximizing the objective for false negatives. In test-time, we rank the evidence (i, j) according to its combined probability of supporting or refuting relevance scorei,j = ∑ y∈{S,R} P
i,j(y). Next, we compute the probability of the claim’s veracity y ∈ {S, R, NEI}. First notice that scores in M are logits (proof in Appendix O)
M i,jw,y = log(C i,j Pi,j(w, y)). (5)
Therefore, we use a learnable extra non-negative degree of freedom Ci,j to compute a linear ensemble7 producing the final probability
P(y) =
∑ i,j,w C
i,j Pi,j(w, y) ∑
y′ ∑ i,j,w C i,j Pi,j(w, y′) . (6)
Lastly, we bias the model to focus only on some tokens in each evidence by enforcing an L2 penalty over the scores in M by
L2 = 1
Le ||M ||2F , (7)
where || · ||F denotes Frobenius norm. We show empirically that this objective leads to significantly better weakly-supervised token-level interpretability (Section 5). Therefore the final per-sample loss with hyperparameters λR, λ2 is
L = − log P(y)− λRLR + λ2L2. (8)

heading: 2.3 Baseline
text: Apart from previous work, we propose a baseline bridging the proposed system and the recent work of Schlichtkrull et al. (2021). In order to apply this recent work for FEVER, we introduce a few necessary modifications8. We normalize all scores in M to compute joint probability across all blocks
P(w, y) = expMw,y∑
w′ ∑ y′ expMw′,y′ . (9)
First, we marginalize out per-token probabilities in each evidence si,j .
P(si,j , y) = ∑
w′∈si,j P(w′, y) (10)
7Assuming y=IRR=NEI. 8The necessity of these is explained in Appendix P.
Using this sentence probability formulation, the objective is computed for every relevant evidence.
Lb0 = 1 |Ap| ∑
si,j ,y∈Ap log P(si,j , y) (11)
Second, unlike Schlichtkrull et al. (2021), we interpolate objective Lb0 with objective
Lb1 = log P(y) = log ∑
si,j
P(si,j , y) (12)
by computing their mean. Like CD, we use Lb1 objective to take advantage of examples from NEI class for which we have no annotation in Ap (and thus Lb0 is virtually set to 0). Unlike CD, the annotations Ap in Lb0 contain only relevant labels where y∗ ∈ {S,R}9.
In order to not penalize non-annotated false negatives, we compute global distribution in Lb0 during training only from representations of tokens from labeled positive and negative sentences in M . In test time, we rank evidences according to scorei,j = ∑ y∈{S,R} P(si,j , y), and predict claim’s veracity according to P(y) =∑ si,j
P(si,j , y). We also considered different model parametrizations discussed in Appendix I.

heading: 2.4 Transferring Supervision to Higher Language Granularity
text: The proposed model can benefit from annotation on the coarser granularity of the language than tested. For example, evidence annotation can be done at the document, block, paragraph, or token level. In Section 5, we show despite the fact that the model is trained on coarse granularity level, the model still shows moderate performance of relevance prediction when evaluated on finer granularity. We demonstrate this with two experiments.
First, the model is trained with sentence-level supervision and it is evaluated on a token-level annotation. For this we leave model as it is — reminding that prior over per-token probabilities enforced by the objective L2 is crucial (Table 4).
Secondly, we assume only block-level annotation is available in training and we evaluate on sentence-level annotation. Here, we slightly alter
9Maximizing NEI class for irrelevant sentences leads to inferior accuracy. This makes sense, since it creates “tug-ofwar” dynamics between Lb0 and Lb1. The former objective tries to allocate mass of joint space in NEI class, since most documents are irrelevant, whereas the latter objective tries to allocate the mass in the dimension of labeled veracity class.
the model, making it rely more on its sentencelevel representations. In Section 5, we show this simple alteration significantly improves the performance at sentence-level. To compute block-level probability, the block is the evidence, therefore the evidence index can be dropped. The probability of the j-th block bj is obtained by marginalizing out the per-token/per-sentence probabilities.
P(bj , y) = ∑
si,j∈bj P(si,j , y) =
∑
si,j∈bj
∑
w′∈si,j P(w′, y)
(13)
In practice, we found it helpful to replace the block-level probability P(bj , y) with its lowerbound P(si,j , y) computed for 1 sentence sampled from the relevant sentence likelihood.
P(bj , y) ≈ P(si,j , y); si,j ∼ p(si,j , y ∈ {S,R}) (14) Intuitively, making a single sentence estimate (SSE) forces the model to invest the mass into a few sentence-level probabilities. This is similar to HardEM10. In LR we then maximize the probabilities of positive blocks computed as in equation 14, and negative sentences11 computed (and normalized) on sentence level as in equation 4.

heading: 2.4.1 Baseline for Token-level Rationales
text: Similarly to Shah et al. (2020); Schuster et al. (2021), we train a masker — a model which learns to replace the least amount of token embeddings at the Claim-Dissector’s input with a single learned embedding in order to maximize the NEI class probability. We compare the unsupervised rationales given by the masker with the unsupervisedly learned rationales provided by the Claim-Dissector on-the-fly. Our masker follows the same architecture as Claim-Dissector. We provide an in-depth description of our masker model and its implementation in Appendix L.

heading: 3 Related Work
text: Datasets. Previous work in supervised opendomain fact-checking often focused on large datasets with evidence available in Wikipedia such as FEVER (Thorne et al., 2018), FEVER-KILT
10In preliminary experiments, we also tried HardEM, but the results over multiple seeds were unstable.
11Indices of irrelevant sentences are mined automatically (see Section 2.1), therefore this supervision comes “for free”.
(Petroni et al., 2021), FAVIQ (Park et al., 2022), HoVer (Jiang et al., 2020) or TabFact (Chen et al., 2020). We follow this line of work and selected FEVER because of its sentence-level annotation, 3 levels of veracity (into S/R/NEI classes), and controlled way of construction — verification should not require world knowledge, everything should be grounded on trusted, objective, and factual evidence from Wikipedia.
Open-Domain Fact-Checking (ODFC) Unlike this work, most of the previous work includes 3- stage systems that retrieve evidence, rerank each document independently, and then make a veracity decision from top-k documents (Thorne et al., 2018; Nie et al., 2019; Zhong et al., 2020).
Jiang et al. (2021) particularly distinguished the line of work which aggregates the final decision from independently computed per-sentence veracity probabilities (Zhou et al., 2019; Soleimani et al., 2020; Pradeep et al., 2021b, inter alia) and the line of work where the top-relevant sentences are judged together to compute the final veracity probability (Stammbach and Neumann, 2019; Pradeep et al., 2021a, inter alia). Jiang et al. (2021) compares similar system against these two assumptions, showing that joint judgment of relevant evidence is crucial when computing final veracity. We stress that our system falls into joint judgment category. Although relevance is computed per sentence, these probabilities along with linear combination coefficients are computed jointly, with the model conditioned on hundreds of input sentences.
To deal with multi-hop evidence (evidence which is impossible to mark as relevant without other evidence) Subramanian and Lee (2020); Stammbach (2021) iteratively rerank evidence sentences to find minimal evidence set, which is passed to verifier. Our system jointly judges sentences within a block, while multi-head attention layer could propagate cross-block information. Our overall peformance results suggest that our system is about on-par with these iterative approaches, while requiring only single forward computation. However, further analysis shows our model underperforms on multi-hop evidence (more in Section 5).
Interpretability Popat et al. (2018); Liu et al. (2020) both introduced systems with an interpretable attention design and demonstrated their ability to highlight important words through a case study. In our work, we take a step further and propose a principled way to evaluate our system
quantitatively. We note that Schuster et al. (2021) proposed a very similar quantitative evaluation of token-level rationales, for data from the VitaminC dataset. The dataset, constructed from factual revisions on Wikipedia, assumed that the revised part of facts is the most salient part of the evidence. In contrast, we instruct annotators to manually annotate terms important to their judgment (Section 4.1). The VitaminC dataset is not accompanied by the evidence corpus, thus we deemed it as unsuitable for open-domain knowledge processing.
Krishna et al. (2022) proposed a system that parses evidence sentences into natural logic based inferences (Angeli and Manning, 2014). These provide deterministic proof of the claim’s veracity. Authors verify the interpretability of the generated proofs by asking humans to predict veracity verdict from them. However, the model is evaluated only on FEVER dataset and its derivatives, which contain potential bias to their approach — the claims in this dataset were created from fact through "mutations" according to natural logic itself.
Joint Reranking and Veracity Prediction Schlichtkrull et al. (2021) proposed a system similar to our work for fact-checking over tables. The system computes a single joint probability space for all considered evidence tables. The dataset however contains only claims with true/false outcomes, typically supported by a single table. While our work started ahead of its publication, it can be seen as an extension of this system.

heading: 4 Experimental Setup
text: Unless said otherwise, we employ DeBERTaV3 (He et al., 2021) as LRM. In all experiments, we firstly pre-train model on MNLI (Williams et al., 2018). We use maximum block-length Lx = 500. Our recipe for implementation and model training is closely described in Appendix N.

heading: 4.1 Datasets
text: FEVER. We validate our approach on FEVER (Thorne et al., 2018) and our newly collected dataset of token-level rationales. FEVER is composed from claims constructed from Wikipedia. Each annotator was presented with an evidence sentence, and first sentence of articles from hyperlinked terms. In FEVER, examples in development set contain multi-way annotation of relevant sentences, i.e., each annotator selected set of sentences (evidence group) they considered relevant. To an-
alyze performance of our components on samples that need multi-hop reasoning, we further created subsets of training/development set. FEVERMH contains only examples where all annotators agreed on that more than 1 sentence is required for verification, whereas FEVERMHART contains only examples, where all annotators agreed that sentences from different articles are required for verification. As majority of examples of FEVERMH are from FEVERMHART , we only evaluate on FEVERMH . We include the subset statistics in Table 1.
TLR-FEVER To validate token-level rationales, we collect our own dataset on random subset of validation set (only considering examples with gold sentence annotation). We collect 4-way annotated set of token-level rationales. The annotators were the colleagues with NLP background from our lab. We instruct every annotator via written guidelines, and then we had 1-on-1 meeting after annotating a few samples, verifying that contents of the guidelines were understood correctly. We let annotators annotate 100 samples, resolve reported errors manually, obtaining 94 samples with fine-grained tokenlevel annotation. In guidelines, we simply instruct annotators to highlight minimal part of text they find important for supporting/refuting the claim. There should be such part in every golden sentence (unless annotation error happened). The complete guidelines are available in Appendix Q.
To establish performance of average annotator, we compute the performance of each annotator compared to other annotators on the dataset, and then compute the average annotator performance. We refer to this as human baseline lower-bound, as each annotator was compared to only 3 annotations, while the system is compared to 4 annotations. We measure performance via F1 metric.
Other Datasets. We further validate our approach on FAVIQ-A (Park et al., 2022) and RealFC (Thorne and Vlachos, 2021) datasets (Appendix A and Appendix B), where it achieves state-ofthe-art results and HoVer (Appendix C), where we demonstrate its limitations on multi-hop evidence.

heading: 4.2 Evaluation
text: Accuracy (A). The proportion of correctly classified samples, disregarding the predicted evidence.
Recall@5 (R@5). The proportion of samples for which any annotated evidence group is matched within top-5 ranked sentences.
FEVER-Score (FS). The proportion of samples for which (i) any annotated evidence group is matched within top-5 ranked sentences, and (ii) the correct class is predicted.
F1 Score measures unigram overlap between predicted tokens and reference tokens, disregarding articles. Having multiple references, the maximum F1 between prediction and any reference is considered per-sample. Our implementation closely follows Rajpurkar et al. (2016).
In practice, both CD and masker model infer continuous scores capturing relevance for every token12. When evaluating F1, we select only tokens with scores greater than threshold τ . We tune the optimal threshold τ w.r.t. F1 on TLR-FEVER.

heading: 5 Results
text: We report results of base-sized models based on 3-checkpoint average. We train only a single large model. We further evaluate retrieval in Appendix M as it is a non-essential part of our contribution.
Performance. We compare the performance of our system with previous work in Table 2. Results marked with ? were unknown/uncertain, and unconfirmed by authors. We note that, apart from HAN (Ma et al., 2019), all previous systems were considering two separate systems for reranking and veracity prediction. Next, we note that only ProofVer system uses additional data. It leverages rewrittenclaim data for fact-correction paired with original FEVER claims (Thorne and Vlachos, 2021).
We observe that (i) even our base-sized RoBERTa-based CD model outperforms base-sized HESM on dev data, and its large version outperforms large-sized KGAT, DREAM and HESM on test data, (ii) our base sized DebertaV3-based CD model matches large-based DREAM and even KGAT with oracle inputs on dev set, (iii) model version with hyperlink expansion (suffixed \w HE) further improves the overall performance, (iv) using larger model improves mostly its accuracy, (v) Claim-DissectorL \w HE achieves better FEVER score than T5-based approach (with
12We consider mask-class logits as scores for masker.
two 3B models) and better accuracy than LongFormer+DebertaXL, but it is not pareto optimal to these previous SOTA, (vi) our model is outmatched by recent ProofVer-SB, though it is more efficient as ProofVer-SB requires two rounds of reranking and autoregressive decoding. We still consider this a strong feat, as our system was focusing on modeling reranking and veracity prediction jointly in an interpretable way. Finally, we inject blocks with missing golden evidence into inputs of ClaimDissectorL \w HE at random positions and measure oracle performance. We observe that items missed by retrieval are still beneficial to the performance.
Ablations. We ablate components of ClaimDissector (CD) in Table 3. Firstly, we resort to single-task training. We drop veracity classification (VC) objective log P(y) or relevance classification (RC) objective LR from the loss. We observe an overall trend — single-task model performs slightly better to multi-task model. The advantages of multitask model, however, lie in its efficiency and ability
to provide explanations between per-evidence relevances and final conclusion. Next, we observe that dropping the L2 objective doesn’t affect the performance significantly. Further, we study the effect of hyperlink expansion (HE) and the effect of multihead (MH) attention layer. As expected, hyperlink expansion dramatically increases performance on FEVERMH . The multi-head attention also brings marginal improvements to results on FEVER. However, contrary to our expectations, there is no effect of the MH layer on FEVERMH , the improvements happened in non-multihop part. Additional experiments with CD on HoVer dataset (Appendix C) confirm this, CD does not work well on examples with cross-article multihop evidence. Improving cross-article reasoning was not our aim, and we leave the investigation to our future research.
Transferring sentence-level supervision to token-level performance. We evaluated the performance of token-level rationales on our dataset in Table 4. We considered two baselines. The first was to select all tokens in golden evidences (Select All Tokens). The second was to select only tokens that overlap with claim tokens (Select Claim Overlaps). We found that our model with weakly-supervised objective produces token-level rationales significantly better13 than the masker — a separate model trained explicitly to identify tokens important to the model’s prediction. We hypothesize that a possible reason for this improvement could be that ad-hoc explainibility methods, that perturb the model’s in-
13See Appendix J for our F1 significance testing protocol.
puts (e.g., masker method or integrated gradients (Sundararajan et al., 2017)) expose the model to out-of-distribution samples, thus degrading its performance and confidence. Contrastively, this isn’t happening by design with in-built interpretability methods like ours. Furthermore, the results also demonstrate the importance of L2 objective. However, human performance is still considerably beyond the performance of our approach.
In Figure 2, we analyze how the performance on FEVER-Score and F1 changes over the course of training on FEVER and TLR-FEVER sets. We find our scores reach the top performance and then detoriate. It is thus necessary do the early stopping on both, the performance metric and the interpretability metric. Furthermore, our experiments shown that tuning of λ2 is crucial, i.e., λ2 = 2e− 3 tuned for DeBERTa-base, achieves no interpretability for the large version (where we use 5e− 4)14.
Interpretable refuting example15 is available in Figure 3. Example shows top-6 refuting sentences ranked by their refuting relevance probability (relevance score) (RS) Pi,j(y = R). Each sentence is prefixed with its Wikipedia article title, RS and prediction score (PS). The prediction score is the corresponding non-negative linear coefficient Ci,j max-normalized between 0 and 1 based on maximum Ci,j for this sample. The token-level relevance, sentence relevance, and sentence prediction score are highlighted on a red-to-black scale (low relevance is black, high relevance is red). Interestingly, the prediction score is highest for sentences
14The sensitivity to λ2 is analyzed in Appendix F. 15We visualized token-level rationales on 100 random dev
set examples at shorturl.at/beTY2.
containing crucial refuting evidence — the number of confirmed kills.
Are prediction scores useful? In Figure 3, a maximum prediction score is assigned to a sentence, that has a lower relevance score than the most relevant document. However, we argue that the sentence with the highest prediction score contains the most relevant information. Hence we formulate the hypothesis that top prediction score better ranks relevance towards final judgment, whereas top relevance score only reflects the model’s confidence of the sentence being somehow relevant. First, we note that scores are highly correlated, but not the same (average Kendall-τ 0.84). Next, we turn to the A/B testing (details in Appendix H), where we select 100 random samples such that: (i) each was correctly predicted, (ii) has a verdict supported or refuted. From these, we select (a) a sentence with the highest prediction score and (b) a sentence with equal or better relevance probability than (a); if there is no such sentence, we don’t include it. We employ 5 annotators to say their preference for sentence (a), sentence (b), or neither (c).
We find that (i) 80 % of annotators unanimously agreed on not preferring (b), (ii) 3 or more annotators in 73 % of cases preferred (a) over (b,c) and finally (iii) the worst single annotator preference for (a) over (a+b) cases was 86 %, demonstrating that human preferences strongly support declared hypothesis.
Transferring block-level supervision to sentence-level performance. The performance of our model on the sentence-level evidences is evaluated in Table 5. We notice that even our vanilla ClaimDissector trained with block supervision reaches
competitive recall@5 on sentence-level. However, adding SSE from equation 14 leads to further improvements both in recall, but also in accuracy. We expected the recall will be improved, because model now focuses on assigning high probability mass only to some sentences within block, since high-entropy of the per-sentence distribution would be penalized in loss. However, we have not foreseen the damaging effect on accuracy, which blocklevel supervision causes. Interestingly, the accuracy without any evidence supervision reported in last row of Table 3 was increased.

heading: 6 Conclusion
text: In this work, we proposed Claim-Dissector, an interpretable probabilistic model for fact-checking and fact-analysis. Our model jointly predicts the supporting/refuting evidence and the claim’s veracity. It achieves state-of-the-art results, while providing three layers of interpretability. Firstly, it identifies salient tokens important for the final prediction. Secondly, it allows disentangling ranking of relevant evidences into ranking of supporting evidence and ranking of refuting evidence. This allows detecting bipolar evidence without being exposed to such bipolar evidence sets during training (Appendix D). Thirdly, it combines the pertoken relevance probabilities via linear combination into final veracity assessment. Therefore it can be identified to what extent the relevance of each token/sentence/block/document contributes to final assessment. Conveniently, this allows to differentiate between the concept of evidence relevance and its contribution to the final assessment. Our work was however limited in experiments with these coefficients, and we would like to analyze what they can learn, and how to inject features, such as satire assessment or source trustworthiness, through these coefficients in our future work.
Finally, it was shown that a hierarchical structure of our model allows making predictions on even finer language granularity, than the granularity the model was trained on. We believe the technique we proposed is transferable beyond fact-checking.
Ethical Considerations
We built the system with the intended use for factchecking support, providing rationales at various level to user for ease of understanding. These rationales include supporting and refuting evidences in the corpus. We see potential misuse of system might lie in spreading of fake news and propaganda by e.g., automatic detection of sources, which support or refute certain claims from the narrative. This could be followed by subsequent glorification/discreditation of statements in these sources. This could influence the human population, but also poison retrieval databases of similar fact-checking systems, influencing their decisions. Future work in this direction, such as Du et al. (2022), needs to study disinformation attacks and how to prevent them.
Limitations
By manual analysis, we found that claim-dissector suffers from overconfidence in blocks with at least 1 relevant evidence. Then it seeks to select more relevant evidences inside, even when they are not. We believe this is connected to how irrelevant negatives are mined in FEVER — they originate only from blocks without relevant evidences.
On real data, the system often struggles to recognize what facts are refuting, and what are irrelevant (especially when applied out-of-domain). We demonstrate this in a case study on downstream application, where we replaced retrieval on Wikipedia with news-media in test-time. We tried to verify the claim "Weapons are being smuggled into Estonia". Our system discovered article with facts about "Weapons being smuggled into Somalia", and used it as a main refuting evidence to predict REFUTE veracity.
Lastly, CD is trained with evidence from Wikipedia, and do not considers other factors important for relevance assessment in practice, such as credibility of source, its reliability, or its narrative. This is the area of active research, as human fact-checkers also need to deal with lies (Uscinski and Butler, 2013).

heading: Acknowledgements
text: We would like to thank to Santosh Kesiraju, Martin Dočekal, Karel Ondřej, Bolaji Yusuf, Michael Schlichtkrull, Juan Pablo Zulag-Gomez, and Apoorv Vyas for reviewing this work or providing helpful discussions. We also thank to Jungsoo
Park and Sewon Min for their prompt responses and help with analysis of FAVIQ-A results.
This work was supported by the Technology Agency of the Czech Republic, project MASAPI (grant no. FW03010656), by CRiTERIA, an EU project, funded under the Horizon 2020 programme, grant agreement no. 101021866, and by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90140).

heading: A Performance on FAVIQ-A
text: To asses more realistic performance of our system, we study its performance on FAVIQ-A (Park et al., 2022). We use the silver passage supervision from Asai et al. (2022), and feed the model with top20 passages retrieved via DPR system (Karpukhin et al., 2020). We keep all the hyperparameters same as for FEVER, and use dev set only for earlystopping. We compare to evidentiality-guided generator (EGG), a t5-based Fusion-in-Decoder (FiD) (Izacard and Grave, 2021) with two decoders from Asai et al. (2022). We use hyperparameters from FEVER. The results are shown in Table 6. Our DebertaV3-based Claim-Dissector reaches state-ofthe-art results on the dataset. The domain mismatch (measured by difference ∆) between development and test set is likely caused by the domain shift of NaturalQuestions test set, from which FAVIQ’s test set was created (see Appendix B in Min et al. (2020)). However, despite our best efforts, we have not uncovered the cause of massive degradation between dev and test set for roberta-base based Claim-Dissector (the standard deviation on test set was only ±0.4 accuracy points).

heading: B Performance on RealFC
text: To analyze, whether our token-level interpretability approach transfers beyond FEVER dataset, and how our model performs in bipolar evidence setting (when claim is both supported by some evidence, but refuted by a different one), we study our model on RealFC (Thorne et al., 2021)16.
Similarly to FS metric on FEVER, RealFC dataset is validated through conditional scoring. It validates both; the performance of reranking and veracity prediction. Specifically, it computes average accuracy/F1 score across samples, while setting per-sample hit/F1 to 0, if the model predicted the wrong veracity. Due to a rather complicated
16Unlike FaVIQ, this dataset also contains sentence-level annotation.
exact definition, official conditional scores are documented in Appendix E.
The results on RealFC are shown in Table 7. For relevant evidence classification, binary F1 computed from the concatenation of all relevant/irrelevant decisions for all sentences is reported. Here supporting or refuting evidence counts as relevant, neutral as irrelevant. For verification, accuracy/macro F1 (denoted VAcc, VF1) are reported. Lastly, we also report conditional scores CondAcc and CondF1, which are aggregated from per-sample binary F1 relevance, set to 0 if the target veracity was not predicted correctly17. We compare CD with a pipelined baseline composed from a 3-class relevance classification (assuming either supporting or refuting evidence is relevant) followed by a veracity classifier (first row). Both components are based on roberta-base (Liu et al., 2019). We also report best number for the corresponding column across all (RoBERTa-based) baselines from Thorne et al. (2021) (second row). We find that in a similar setup, CD improves only marginally over baseline. The early stopping of baselines and the third row is performed on VAcc. Row 4 and further report on results early stopped on CondF1, as we found CondF1 to correlate with the majority of the metrics. To counter data imbalance, from row 4, we use veracity class weighting. Using identity-weighting (each weight is 1), we observe accuracy to be maximal, whereas using the inverse-class-prior weighting exactly as in the previous chapter we found F1 to perform the best. However, for maximizing conditional scores, we found the weighting corresponding to average of identity-weighting and inverse-class-prior to work the best, and so we report these further.
We find that CD-based systems retain its interpretability18 with class weighting sets a new stateof-the-art on the dataset. Additionally, we uncover a large performance boost on the subset of the dataset with annotated bipolar evidence (last two rows).

heading: C Performance on HoVer
text: To study how well our model can deal with claims, which require multihop information, we trained our system on HoVer (Jiang et al., 2020). In particular, we follow the recipe for Baleen (Khattab
17Due to rather complicated exact definition, official conditional scores are documented in Appendix E.
18Qualitative samples with token-level rationales on bipolar subset of RealFC are available at https://shorturl.at/hGU67.
et al., 2021) and retrieve 4×25 top articles using official quantized Baleen implementation19 (which achieves about 2 % lower retrieval@100 on supported samples than reported in paper). We split 5 starting documents from each iteration into blocks, padding input with further documents from first retrieval iteration when necessary. We keep input size at K1 = 35, and we do not use hyperlink expansion. We compute the probability of not-supported class by summing NEI and REFUTE classes. Furthermore, we assume simplified conditions, we infuse inputs with oracle information when necessary (achieving RaI 100 %) and predict as many evidences, as there was annotated. We refer reader to Khattab et al. (2021) for further information about setup and evaluation metrics.
Nevertheless, our system lags behind Baleen on 3 and 4 hop examples, as shown in Table 8. We hyphothesize that, similarly to Baleen, autoregressive process is necessary to match its performance. We leave the question of interpretable multi-hop fact-checking with Claim-Dissector open for our future work.
19https://github.com/stanford-futuredata/Baleen

heading: D Detection of examples with bipolar evidence.
text: We manually analyzed whether we can take advantage of model’s ability to distinguish between evidence, which is relevant because it supports the claim, and the evidence which is relevant because it refutes the claim. To do so, we try to automatically detect examples from the validation set, which contain both, supporting and refuting evidence (which we refer to as bipolar evidence). We note that there were no examples with explicitly annotated bipolar evidence in the training data.
We select all examples where model predicted at least 0.9 probability for any supporting and any refuting evidence. We found that out of 72 such examples, 66%(48) we judged as indeed having the bipolar evidence20. We observed that about half (25/48) of these examples had bipolar evidence because of the entity ambiguity caused by opendomain setting. E.g., claim “Bones is a movie” was supported by sentence article “Bones (2001 film)” but also refuted by sentence from article “Bones (TV series)” and “Bone” (a rigid organ).

heading: E Conditional Scoring on RealFC Dataset
text: First, binary F1 is computed for each i-th example from the dataset—section with several sentences, and every sentence is predicted to be relevant (i.e., supporting or refuting) or irrelevant (i.e., neutral)— obtaining F1 score s(i). Define I[m(i) = li] as an indicator function yielding 1 if model mv predicted the veracity correct class li for an i-th example, and 0 otherwise. With dataset of size N , the CondAcc is defined as an average F1 score for samples with correct veracity
CondAcc = 1
N
N∑
i=1
s(i)I[mv(i) = li]. (15)
20Annotations are available at shorturl.at/qrtIP.
Next, assume that TPs, TPr, and TPn and similarly FPx and FNx are TPs, FPs, and FNs computed for each veracity class—support veracity (s), refute veracity (r), or neutral veracity (n), and x ∈ {s, r, n}. Further, assume that wTPs, wTPr, wTPn are computed as wTPx =∑N
i=1 s(i)I[mv(i) = li ∧ x = li], where x ∈ {s, r, n}. Firstly, weighted F1 wF1x is computed for each class separately from precision px and recall rx as shown in Equation 16. The CondF1 score is obtained by averaging wF1x across all classes x ∈ {s, r, n}.
px = wTPx
TPx + FPx rx = wTPx TPx + FNx
wF1x = 2 pxrx
px + rx
(16)

heading: F Sensitivity to λ2 Weight
text: In Figure 4, we analyze the sensitivity of λ2 parameter on F1 performance on TLR-FEVER during training. Choosing the large weight may lead to instabilities and vanishing of interpretable rationales, choosing smaller weight delays the peak performance in terms of F1. Note that for DeBERTaV3large we chose λ2 = .0005, as the one we used for base version (λ2 = .002) leaded to such vanishing.

heading: G Structure of Single-layer Perceptron
text: Given a vector x, the structure of single-layer perceptron from equation 1 is the following:
SLP(x) = GELU(dp(W ′ lnorm(x))). (17)
The operator dp denotes the dropout (Srivastava et al., 2014) used in training, W ′ is a trainable matrix, GELU is the Gaussian Error Linear Unit (Hendrycks and Gimpel, 2016) and lnorm is the layer normalization (Ba et al., 2016).

heading: H Details on Analysis of Prediction Scores
text: We define relevance score (RS) as Pi,j(y = l) where l ∈ {S,R} is the ground truth label. For A/B testing, we shuffle (a) and (b) cases for annotators, so they cannot determine which sentence comes from which source. An example of 1 annotation is available at https://shorturl.at/hiCLX. Since we found many annotators hesitated with no preference option (c) when computing Krippendroff’s α, we assume two classes, and empty preference when the annotator does not have a preference (case (c)) (we do not consider it a separate nominal category). Krippendorff’s α=0.65 achieved moderate agreement.
To compute average Kendall τ , we select sentences with RS>0.7 for each example; this creates relevance ranking. Then we reorder selected sentences according to PS, obtaining prediction ranking. Kendall τ is computed for every sample, and the resulting statistic is averaged across all samples in the FEVER validation set.

heading: I Experiments with Different Model Parametrizations
text: Apart from parametrizations provided in the main paper, we experimented with several different parametrizations described below. We keep the training details the same as for our baseline (Section 2.3). Starting off with a baseline system formulation, we will consider replacing Lb0 with different objective functions.
Lb2 = 1 |A| ∑
si,j ,y∈A log P(si,j , y) (18)
With Lb2, the annotation set A contains both relevant and irrelevant annotations. We found in practice this does not work - recall@5 during training stays at 0. This makes sense since if annotation exists, the final class is likely support or refute. Drifting the probability mass towards NEI for irrelevant annotations is adversarial w.r.t. total veracity probability.
Lb3 = log ∑
si,j ,y∈Ap P(si,j , y) (19)
Instead of maximizing the multinomial probability, Lb3 objective marginalizes over relevant annotations.
Lb4 = log ∑
si,j∈Ap
∑
y
P(si,j , y) (20)
Additionally to Lb3, Lb4 also marginalizes out the class label y.
The results in Table 9 reveal only minor differences. Comparing Lb3 and Lb4, marginalizing out label improves the accuracy, but damages the recall. Baseline parametrization achieves best accuracy but the worst recall. Claim-Dissector seems to work the best in terms of FS, but the difference to Lb3 is negligible, if any.

heading: J Statistical Testing on F1 Measure
text: To compare CD with masker in F1, we follow Goutte and Gaussier (2005), sum TPs, FPs, FNs across the dataset, estimate recall (R) and precision (P) posteriors, and sample F1 distributions. To obtain sample of average F1 from multiple checkpoints, we estimate the P and R posteriors for each checkpoint separately, sample F1 for each checkpoint and then average these. We estimate p ≈ P(F1a >F1b) via Monte-Carlo, and consider significance level at p > 0.95.

heading: K The Continued Influence Effect: Retractions Fail to Eliminate the Influence of Misinformation
text: Lewandowsky et al. (2012) summarizes research paradigm, which focuses on credible retractions in neutral scenarios, in which people have no reason to believe one version of the event over another. In this paradigm, people are presented with a factious report about an event unfolding over time. The report contain a target piece of information (i.e. a claim). For some readers, the claim is retracted, whereas for readers in a control condition, no correction occurs. Then the readers are presented with a questionnare to assess their understanding of the
event and the number of clear and uncontroverted references to the claim’s veracity.
In particular, a stimulus narrative commonly used within this paradigm involves a warehouse fire, that is initially thought to have been caused by gas cylinders and oil paints there were negligently stored in a closet. A proportion of participants is then presented with a retraction such as "the closet was actually empty". A comprehension test follows, and number of references to the gas and paint in response to indirection inference questions about the event (e.g., "What caused the black smoke?") is counted.
Research using this paradigm has consistently found that retractions rarely, if ever, had the intended effect of eliminating reliance on misinformation, even when people remember the retraction, when later asked. Seifert (2002) have examined whether clarifying the correction might reduce the continued influence effect. The correction in their studies was strengthened to include the phrase "paint and gas were never on the premises". Results showed that this enhanced negation of the presence of flammable materials backfired, making people even more likely to rely on the misinformation in their responses. Some other additions to the correction were found to mitigate to a degree, but not eliminate, the continued influence effect. For instance, when participants were given a rationale about how misinformation originated, such as "a truckers’ strike prevented the expected delivery of the items", they were less likely to make references to it. Even so, the influence of the misinformation could still be detected. The conclusion drawn from studies on this phenomenon show that it is extremely difficult to return the beliefs to people who have been exposed to misinformation to a baseline similar to those of people who have never been exposed to it. We recommend reading Lewandowsky et al. (2012) for broader overview of the misinformation and its correction.

heading: L Masker
text: Model Description. Our masker follows same DeBERTaV3 architecture as Claim-Dissector, except that the multiheaded layer from the equation (1) is omitted. It receives K1 blocks at its input, encoded the very same way as for the Claim-Dissector. Instead of computing matrix M— which contains three logits per evidence token, the masker predicts two logits [li0, l i 1] — corresponding
to keep/mask probabilities [pi0, p i 1] for i-th token in evidence of every block. The mask [mi0,m i 1] is then sampled for every token from concrete distribution via Gumbel-softmax (Jang et al., 2017). During training, i-th token embedding ei at the Claim-Dissector’s input e′i is replaced with a linear combination of itself and a learned maskembedding em ∈ Rd, tuned with the masker.
e′i = m i 0ei +m i 1em (21)
The masker is trained to maximize the ClaimDissector’s log-likehood of NEI class, while forcing the mask to be sparse via L1 regularization. Per-sample objective to maximize with sparsity strength hyperparameter λS is given as
L = log P(y = NEI)− λS Le
∑
i
|mi0|. (22)
Training Details. We keep most hyperparameters the same as for Claim-Dissector. The only difference is learning rate 2e − 6, adaptive scheduling on Gumbel-softmax temperature τ and training model/masker on different dataset split. Training starts with temperature τ = 1 and after initial 100 steps, it is linearly decreasing towards τ = 0.1 at step 700. Then we switch to hard Gumbel-softmax — sampling 1-hot vectors in forward pass, while computing gradients as we would use a soft sample with τ = 0.1 at backward pass. We randomly split training set and we train model on 75 % of data, and masker on remaining 25 % of data.

heading: M Retrieval Performance
text: We evaluate the retrieval method from Jiang et al. (2021) and the proposed hyperlink expansion method in Table 10. We use two metrics:
Recall@Input (RaI). We evaluate retrieval w.r.t. recall at model’s input while considering different
amount of K1+K2 blocks at the input, i.e. the score hit counts iff any annotated evidence group was matched in K1+K2 input blocks.
Number of Sentences@Input (#SaI) denotes average number of sentences at model’s input under corresponding K1 +K2 setting.
We focus on analyzing the effect of hyperlink expansion, varying K2, while keeping K1 = 35 in most experiments, which is setting similar to previous work — Jiang et al. (2021) considers reranking top-200 sentences. We observe that setting K1 + K2 = 35 + 10 already outperforms retrieval without hyperlink expansion and K1 = 100 blocks. Such observation is thus consistent with previous work which used hyperlink signal (Hanselowski et al., 2018; Stammbach and Neumann, 2019).
An in-depth evaluation of retrieval method adopted from Jiang et al. (2021) is available in Table 11.

heading: N Experimental Details
text: We base our implementation of pre-trained language representation models on Huggingface (Wolf et al., 2019). Unless said otherwise, we employ DeBERTaV3 (He et al., 2021) as LRM. In all experiments, we firstly pre-train model on MNLI (Williams et al., 2018). While we observed no significant improvement when using a MNLI-pretrained checkpoint, we found that without MNLI pretraining, our framework sometimes converges to poor performance. We train model on FEVER with minibatch size 64, learning rate 5e−6, maximum block-length Lx = 500. We schedule linear warmup of learning rate for first 100 steps and then keep constant learning rate. We use Adam with decoupled weight decay (Loshchilov and Hutter, 2017) and clip gradient vectors to a maximal norm of 1 (Pascanu et al., 2013). In all experiments, the model is trained and evaluated in mixedprecision. We keep λR = λ2 = 1. We use 8x
Nvidia A100 40GB GPUs for training. We validate our model every 500 steps and select best checkpoint according to FEVER-Score (see Subsection 4.2). We have not used any principled way to tune the hyperparameters.
To train model with SSE, we decrease the strength of block-level supervised LR objective to λR = 0.7. We switch between vanilla objective and SSE objective randomly on per-sample basis. Training starts with replace probability psse = 0. for first 1, 000 steps. The probability is then linearly increased up to psse = 0.95 on step 3, 000, after which it is left constant.
All results except for Table 3 and Table 4 were early-stopped based on the best FS. For Table 3, we report best result for each metric early-stopped independently, to be comparable with ablations where FS was not available. For Table 4, we record best F1 during training.

heading: O Logit Proof
text: The link between equation 2 and equation 5 can be easily proved as follows. Applying logarithm to equation 2 we get
log Pi,j(w, y) = M i,jw,y− log ∑
w′
∑
y′ expM i,jw′,y′ .
(23) Expressing M i,jw,y, substituting C
i,j =∑ w′ ∑ y′ expM i,j w′,y′ , and merging the logarithms, we arrive to equation 5. We recommend Bishop (2006), chapter 4.2 for further information.

heading: P The Necessity of Baseline Modifications
text: The reason for the modification is that (i) the original model (Schlichtkrull et al., 2021) (without Lb1) could not benefit from NEI annotations present on FEVER, resulting in unfair comparison with our models and previous work, as TabFact does not contain such annotations (ii) the original model is not able to distinguish the attribution from the repeated relevant evidence, because equations (6)/(9) in their work just sum the probabilities of relevant items supervised independently — they do not use the supervision of overall veracity for the claim. This is problematic especially in FEVER setting comparable to ours, where the relevance of hundreds of sentences is considered (many of them possibly relevant) as compared to TabFact where only top-5 retrieved tables were considered, and often only single is relevant.

heading: Q Token-level Annotation Guidelines
text: Annotation Guidelines Welcome to the “Pilot annotation phase” and thank you for your help! How to start annotate If you haven’t done so, simply click on "Start Annotation" button, and the annotation will start. Annotation process & Guidelines
• In pilot annotation, we are interested in annotator’s disagreement on the task. So whatever disambiguity you will face, do not contact the organizers but judge it yourself.
• Your task is to annotate 100 samples. In each case, you will be presented with list of sentences divided by | character. The sentences do not need to (and often do not) directly follow each other in text. Be sure that in each case you:
• read the claim (lower-right corner)
• read metadata - to understand the context, you also have access to other metadata (lowerright corner), such as
– titles - Wikipedia article names for every sentence you are presented with, split with character |,
– claim_label - Ground-truth judgment of the claim’s veracity.
• highlight minimal part of text you find important for supporting/refuting the claim. There should be such part in every sentence (unless annotation error happened). PLEASE DO NOT ANNOTATE ONLY WHAT IS IMPORTANT IN THE FIRST SENTENCE.
• Use "RELEVANT" annotation button highlight the selected text spans.
• In some cases, you can find errors in the ground-truth judgment, in other words, either document is marked as supported and it should be refuted according to your judgment or vice-versa. If you notice so, please annotate any part of the document with CLAIM_ERROR annotation.
• In case you would like to comment on some example, use comment button (message icon). If the comment is example specific, please provide specific example’s id (available inbetween metadata).
FAQ
• The example does not contain enough information to decide whether it should be supported or refuted. Should I label it as a CLAIM_ERROR? No. In such case, please annotate parts of the input, which are at least partially supporting or refuting the claim. Please add comment to such examples. If there are no such input parts, only then report the example as CLAIM_ERROR.
That is it. Good luck!
ACL 2023 Responsible NLP Checklist

heading: A For every submission:
text: A1. Did you describe the limitations of your work?
Left blank.
A2. Did you discuss any potential risks of your work? Left blank.
A3. Do the abstract and introduction summarize the paper’s main claims? Left blank.
A4. Have you used AI writing assistants when working on this paper? Left blank.

heading: B Did you use or create scientific artifacts?
text: Left blank.
B1. Did you cite the creators of artifacts you used? Left blank.
B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank.
B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Left blank.
B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Left blank.
B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Left blank.
B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank.

heading: C Did you run computational experiments?
text: Left blank.
C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank.
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.
C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank.
C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank.
C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank.

heading: D Did you use human annotators (e.g., crowdworkers) or research with human participants?
text: Left blank.
D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Left blank.
D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Left blank.
D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Left blank.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Left blank.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Left blank.

heading: Future Work
text: We leave the question of interpretable multi-hop fact-checking with Claim-Dissector open for our future work. 19https://github. com/stanford-futuredata/Baleen