Overview and Insights from the SciVer Shared Task on Scientific Claim Verification

We present an overview of the SciVer shared task, presented at the 2nd Scholarly Document Processing (SDP) workshop at NAACL 2021. In this shared task, systems were provided a scientific claim and a corpus of research abstracts, and asked to identify which articles SUPPORT or REFUTE the claim as well as provide evidentiary sentences justifying those labels. 11 teams made a total of 14 submissions to the shared task leaderboard, leading to an improvement of more than +23 F1 on the primary task evaluation metric. In addition to surveying the participating systems, we provide several insights into modeling approaches to support continued progress and future research on the important and challenging task of scientific claim verification.


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1 Introduction

Due to both rapid growth in scientific publications and the proliferation of mis- and dis-information online, there is a growing need for automated systems that can assist humans in assessing the veracity of scientific claims with evidence found in the research literature. For the SCIVER shared task, systems are given a claim about a scientific entity and a corpus of abstracts from peer-reviewed research articles, and are asked to identify the articles in the corpus that Support or Refute the claim; each prediction is required to be accompanied with evidentiary sentences, or rationales, from the abstract that justify the labeling decision.

This task poses various challenges to systems. For example, entailment relationships between claims and evidences found in scientific writing are often complex, and understanding them to arrive at a correct Support or Refute

labeling decision may require reasoning about numerical quantities, increases and decreases in measurements, or causal chains. Furthermore, since annotations require scientific expertise, training data for this task are scarce. As a result, systems must employ techniques to overcome the lack of training data, such as domain adaptation or transfer learning.

Here, we report the results on the SCIVER shared task. A total of 11 teams made 14 submissions to the shared task leaderboard, leading to a collective improvement of +23 F1 on the primary evaluation metric, compared to previous baselines. The remainder of this report is organized as follows: In §2, we describe the shared task setup, including the choice of dataset, task definition, and evaluation metrics. In §3, we provide an overview of the systems submitted for the task, and highlight unique features of individual systems. In §4, we present the results from the shared task evaluation. Finally, in §5, we identify several insights into modeling approaches demonstrated by participating systems.

System Name Team affiliations Associated paper
VerT5erini University of Waterloo Pradeep et al. (2021)
ParagraphJoint UT Dallas / Chan Zuckerburg Initiative / UCLA Li et al. (2021)
Law & Econ ETH Zurich Stammbach and Ash (2020)
QMUL-SDS Queen Mary University of London Zeng and Zubiaga (2021)
first_1 Ping An of China -
SciKGAT Tsinghua University / Microsoft Research Liu et al. (2020a)
bioBert for sciFact - -
JC_UKP TU Darmstadt -
sum_rationale UC Irvine -
base_3_4 - -
pasic_scibert_tfidf Ping An International Smart City -
VeriSci Allen Institute for AI Wadden et al. (2020)
Table 1: Systems that submitted to the SCIVER leaderboard. Alongside system names are affiliations of the associated team (if applicable) and any papers associated with the submission (if applicable).

2 Shared task description

We briefly describe the dataset, task, and evaluation. Additional details on the data collection process and evaluation metrics can be found in Wadden et al. (2020).


We use the SciFact dataset from Wadden et al. (2020). SciFact consists of 1,409 claims with train, dev, and test splits of 809, 300, and 300 claims respectively. The full train and dev sets and test claims were publicly available six weeks prior to opening the submission portal; gold evidences and labels in the test set are not released publicly. Each claim is an assertion about a single biomedical entity or process, curated by a biomedical expert. These claims are verified by other biomedical experts against a corpus of 5,183 abstracts from peer-reviewed biomedical research articles. For each claim, relevant abstracts are those annotated with evidentiary sentences that Support or Refute the claim.


Given an input claim, the task is to (i) identify all abstracts in the corpus that are relevant to the claim, (ii) label the relation of each relevant abstract to the claim as either Supports or Refutes, and (iii) identify a set of evidentiary sentences (i.e. rationales) to justify each label.


Systems are evaluated by their F1 score for correctly identifying relevant abstracts. We report two evaluation metrics: abstract-level F1 rewards a system for identifying and labeling abstracts correctly. A system may predict up to three evidentiary sentences for each abstract. As long as these three sentences contain a gold rationale, the system is scored as correct; this is similar to the FEVER score from Thorne et al. (2018). In contrast, sentence-level F1 rewards a system for identifying and labeling individual evidentiary sentences correctly, similar to the “conditional score” introduced in Thorne et al. (2021). Unlike abstract-level evaluation, this metric penalizes models for over-predicting evidentiary sentences. In practice, we find that systems rank similarly in terms of sentence-level and abstract-level performance.

Model Submissions

Submissions were made through a publicly-available online leaderboard111https://leaderboard.allenai.org/scifact. To prevent overfitting on the test set, teams were permitted to make one submission per week. The leaderboard was available for seven weeks before final submissions were due.

3 Overview of systems

Table 1 presents the submitted systems. As the online leaderboard is still accepting new submissions, we only include in this report the systems that were present by the shared task submission deadline.

3.1 Modeling approaches

All systems for which model descriptions are available use a three-stage pipeline consisting of (1) retrieval of relevant abstracts, (2) selection of evidentiary sentences from retrieved abstracts, and (3) label prediction using the identified rationales. Many teams introduced improvements to these three components, which we summarize here.

Abstract retrieval

Most systems rely on “bag-of-words” approaches such as TF-IDF or BM25 Robertson and Zaragoza (2009) to retrieve an initial set of candidate abstracts. In contrast, ParagraphJoint computes the distance between “dense” claim and abstract representations using BioSentVec Chen et al. (2019).

Some systems further refine the initial set of retrieved abstracts using a neural model: Vert5Erini uses T5 Raffel et al. (2020) while SciKGAT uses BERT Devlin et al. (2019) to re-rank retrieved candidates. QMUL-SDS uses a BioBERT Lee et al. (2020)

text classifier for candidate filtering.

Evidence selection

Systems take one of two approaches: single-sentence systems predict whether a given sentence was evidence based on the claim and the sentence alone. Vert5Erini uses T5, QMUL-SDS uses BioBERT and SciKGAT uses BERT to produce representations for each claim-sentence pair.

The full-document systems encode the claim together with the entire abstract, and make predictions for each sentence based on this full-document encoding222Abstracts longer than 512 tokens are shortened by truncating long sentences. The ParagraphJoint model interleaves [SEP] tokens between sentences, encodes the entire abstract using RoBERTa Liu et al. (2019), and finally obtains sentence representations through self-attention pooling over words within each sentence. The Law & Econ model treats rationale selection as a sequence tagging task, using a SciBERT Beltagy et al. (2019) token-level tagger: any sentence with at least one predicted positive token is taken as evidence.

Label prediction

Systems predict Support, Refute, or NEI (Not Enough Info) labels by concatenating the claim and all selected evidentiary sentences and feeding it through a neural three-way text classifier. Unless otherwise noted, systems tend to use the same model class for evidence selection and label prediction stages (e.g., for both pipeline stages, Vert5Erini use T5 and ParagraphJoint use RoBERTa).

The SciKGAT system, which uses BERT for evidence selection, switches to using a kernel graph attention network Liu et al. (2020b) for aggregating sentences for label prediction. We note the ParagraphJoint team report experimenting with a similar approach but opt not to use it in their final system due to lack of positive results Li et al. (2021).

The QMUL-SDS system switches from BioBERT to RoBERTa for the label prediction stage. Furthermore, the model improves classification performance using a two-stage approach – first classifying abstracts as either Containing Evidence or Not, and then classifying evidence-containing abstracts as Supported or Not.

3.2 Model training techniques

We summarize several helpful techniques that teams used to improve model training when developing their systems.

Transfer learning

Vert5Erini, Law & Econ, ParagraphJoint, and SciKGAT models were first trained on the FEVER dataset Thorne et al. (2018) before training on SciFact. In contrast, QMUL-SDS was only trained on SciFact. SciKGAT performed additional language model pretraining on data from the CORD-19 dataset Wang et al. (2020).

Negative sampling

Teams devised a number of strategies to expose the model to “negative samples” – abstracts that do not contain evidence relevant to a given claim. The Vert5Erini team used non-evidence sentences from relevant abstracts as negative samples.

The ParagraphJoint team used irrelevant abstracts with high lexical similarity to “gold” relevant abstracts as additional negatives. Their system was trained using 12 negative abstracts per positive abstract, allowing it to maintain high precision while retrieving a larger number of candidate abstracts for each claim. This system also used scheduled sampling, in which the label predictor is given gold rationales early in training and gradually transitions to using predicted rationales; this was found to increase model robustness.

Along similar lines, the QMUL-SDS team trained their evidence selection and label prediction components with retrieved abstracts rather than just using gold abstracts.

Dev set usage

Both Vert5Erini and ParagraphJoint

teams found it beneficial to perform initial hyperparameter selection on the dev set, and then train a final model on the train and dev sets combined. This is unsurprising given the moderate size of the

SciFact training set.

4 Results

Sentence-level Abstract-level
Selection+Label Label+Rationale
Submission P R F1 P R F1
VerT5erini (Neural) (Train+Dev) 60.59 66.49 63.40 62.85 71.62 66.95
ParagraphJoint 68.94 54.59 60.94 73.66 61.71 67.16
VerT5erini (Neural) 60.00 57.57 58.76 61.47 63.96 62.69
Law & Econ 56.63 55.41 56.01 62.80 58.56 60.61
VerT5erini (BM25) 58.33 52.97 55.52 60.28 58.11 59.17
QMUL-SDS 66.17 47.57 55.35 72.97 48.65 58.38
first_1 65.06 47.30 54.77 65.36 52.70 58.35
SciKGAT 61.15 42.97 50.48 76.09 47.30 58.33
bioBert for sciFact 54.87 45.68 49.85 58.29 49.10 53.30
JC_UKP 40.69 41.35 41.02 50.24 47.75 48.96
sum_rationale 34.45 47.30 39.86 42.38 51.35 46.44
sum_rationale 28.45 52.97 37.02 35.34 55.41 43.16
base_3_4 5.87 55.95 10.63 9.54 50.45 16.05
pasic_scibert_tfidf 7.95 14.32 10.22 10.11 16.22 12.46
VeriSci 38.56 40.54 39.53 46.61 46.40 46.50
VeriSci Zero-Shot 23.71 31.08 26.90 42.26 31.98 36.41
Table 2: System performance on the SCIVER shared task. Systems are ordered by sentence-level F1. For Vert5Erini, (Neural) indicates that a neural re-ranker was used for retrieval, and (Train+Dev) indicates that the model was trained on both the train and dev sets as described in §3.2.

Table 2 presents performance of all submissions on the public leaderboard during the seven week shared task period. Vert5Erini performs best on sentence-level evaluation, achieving an improvement of +23.87 F1 (+60.4%) for sentence-level evaluation relative to the VeriSci baseline. ParagraphJoint performs best on abstract-level evaluation, improving over the VeriSci by +20.66 F1 (+44.4%). Using the dev set as additional training data provides a substantial boost; this strategy alone improves Vert5Erini performance by roughly +5 F1. While Vert5Erini achieves higher recall, ParagraphJoint has higher precision; this is likely because ParagraphJoint was exposed to more negative samples during training.

5 Insights

A number of participating teams reported performance on individual pipeline components in publications associated with their systems (see Table 1). Based on their reports and discussions with shared task participants, we highlight findings on several modeling decisions that have had significant impact on results in the shared task.333Metrics reported in this section are self-reported by participants and have not been verified by the task organizers. Furthermore, metrics reported in this section are not directly comparable to those in Table 2 as the main results evaluate end-to-end system performance while the metrics reported here are ablations with respect to a pipeline component.

Neural refinement of abstract candidates

The original VeriSci model uses TF-IDF to retrieve the top documents for each claim, but shared task participants have demonstrated substantial improvement over “bag-of-words” retrievers using neural refinement. For instance, the Vert5Erini neural re-ranker improves Recall@3 from the TF-IDF score of 69.4 to 86.6, a +24.8% increase. The QMUL-SDS system, which uses a binary classifier to filter for relevant abstracts, achieves an F1 of 74.2, while TF-IDF with a Top-3 strategy for identifying relevant abstracts only gets 26.3 F1.

Can we simply replace the bag-of-words component with retrieval based on pre-trained dense representations of? Not yet. The ParagraphJoint team, which entirely replaced bag-of-words with dense embeddings obtained using BioSentVec Chen et al. (2019), showed that abstract retrieval performance is slightly worse than simple TF-IDF, with a Recall@3 of 67.0. The QMUL-SDS team also informally reported negative results experimenting with pre-trained DPR Karpukhin et al. (2020), another dense retrieval technique. These shared task findings comparing bag-of-words, neural refinement, and dense-only retrieval on SciFact abstract retrieval have also been demonstrated in other work (see Table 2 in Thakur et al. (2021)). Adapting pre-trained dense retrieval models to SciFact remains an interesting area for future work.

Surrounding context for evidence selection

ParagraphJoint and Law & Econ demonstrate positive results when incorporating full-abstract context during evidence selection, compared to independent assessment of each sentence. In an ablative analysis using oracle abstracts, the ParagraphJoint team reports that incorporating full-abstract context increases evidence selection performance from 65.3 F1 to between 68.8 and 71.7 F1, relative improvements of +5.4% to +9.8%.

Reliance on larger models

These shared task results demonstrate the unreasonable effectiveness of simply using larger models to improve performance. In particular, Vert5Erini achieves substantial improvements while preserving much of the model architecture from the original VeriSci baseline, but swapping in the larger T5 model. In an ablative experiment using gold evidence, the Vert5Erini team reports that a label predictor that uses T5-3B achieves a performance of 88.2 F1, compared to 80.2 F1 when using RoBERTa-large, a +10.0% relative improvement. They observe similar improvements on evidence selection as well. In fact, Wadden et al. (2020) even reports higher performance by RoBERTa-large than SciBERT, where the former has 3 times more parameters, despite the latter being trained on in-domain data.

Still, ParagraphJoint demonstrates a surprising result in achieving performance competitive to Vert5Erini using RoBERTa-large (355M parameters, 10% of T5-3B size), showing that other modeling strategies (e.g. negative sampling) can have significantly benefit system performance while keeping computational burden low. First, this result suggests the need for an improved evaluation setup for the SCIVER task that accounts for model weight classes for submissions and making comparisons. Second, we postulate the need for a follow-up study investigating whether specific modeling strategies employed by smaller models would still translate to significant improvements when using larger models like T5.

6 Conclusion

The SCIVER shared task on scientific claim verification received 14 submissions from 11 teams. The submitted systems achieved substantial improvements on all three stages of the scientific claim verification pipeline – abstract retrieval, evidentiary sentence identification, and label prediction – improving on the previous state-of-the-art by 60% and 44% on the two primary evaluation metrics. We’ve surveyed the various approaches taken by participants and discussed several findings that have emerged from this shared task. Looking forward, the strong performance of systems on SCIVER suggests it may be time to explore other more ambitious challenges on the path toward building real-world scientific claim verification systems. For instance, future work could study retrieval from a much larger scientific corpus, approaches to consolidate evidence from multiple documents while taking into account the strength and credibility of different evidence sources, or techniques for building efficient systems that can approximate performance of the top submissions at a fraction of the cost.


We thank all the teams for their participation in the SCIVER shared task. We are especially grateful to Ronak Pradeep and Xueguang Ma (Vert5Erini), Xiangci Li (ParagraphJoint), Dominik Stammbach (Law & Econ), Xia Zeng (QMUL-SDS), and Zhenghao Liu and Chenyan Xiong (SciKGAT) for their helpfulness in providing details about their systems and for participating in workshop presentations and discussions. We also thank the organizing committee of the SDP 2021 workshop for hosting the SCIVER shared task and for their feedback and assistance in helping us organize this event. We finally thank Iz Beltagy, Arman Cohan, Hanna Hajishirzi, and Lucy Lu Wang from AI2 for their help with reviewing, shared task insights and feedback in writing this report.


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