Determining the Credibility of Science Communication
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Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible -- e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. I will present some first steps towards addressing these problems and outline remaining challenges.
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Determining the Credibility of Science Communication
- 1. Determining the Credibility of Science Communication Isabelle Augenstein* augenstein@di.ku.dk @IAugenstein http://isabelleaugenstein.github.io/ *partial slide credit: Dustin Wright SDP Workshop 10 June 2021
- 3. Supporting the Life Cycle of Research 26/08/2021 3 Reviewing Support Citation Analysis Writing Assistance Information Discovery Conducting Experiments Paper Writing Peer Review Research Impact Tracking Information Extraction Summarisa tion Citation Prediction Reviewer Matching Review Score Prediction Citation Prediction Citation Trend Analysis
- 4. Scholarly Document Processing • Goal: to automatically process scientific text to support scholars • Example NLP tasks • Extract information about scientific concepts, e.g. drugs and proteins • Recommend relevant papers to cite • Challenges • Supervised learning is hard: annotation is expensive, requiring domain experts • Language used is diverse across fields • Different modalities • Meta-data also important 26/08/2021 4
- 5. 26/08/2021 5 Press Release BBC DailyMail The Express Etc... Credibility and Veracity of Science Communication
- 6. Fact Checking 26/08/2021 6 Focus on veracity What about more subtle forms of misinformation?
- 7. Credibility and Veracity of Science Communication • Shortcomings of prior work • Assumes scientific writing is credible • Assumes claims made are supported by underlying evidence • Examples issues • When writing a paper • Making claims not backed up by literature • Missing important citations • Presenting conclusions not supported by data • Popular science communication • Distortion of findings • Exaggerations • Outright misrepresentations 26/08/2021 7
- 8. • Cite-worthiness detection • Detecting if a sentence should include a citation to prior work • Useful for assistive writing of scientific papers • Similar to claim detection in fact checking • Exaggeration detection • Detecting if a news article exaggerates claims made in a scientific paper • Useful for assistive writing & quality check of press releases • Related to veracity prediction, but more nuanced task Challenges Addressed In This Talk
- 9. Overview of Today’s Talk • Introduction • The Life Cycle of Scientific Research • Part 1: Cite-Worthiness Detection • The CiteWorth dataset • Methods for cite-worthiness detection • Part 2: Exaggeration Detection • Task framing • Semi-supervised learning for exaggeration detection • Conclusion • Future research challenges
- 10. CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding Dustin Wright, Isabelle Augenstein ACL 2021 (Findings) 10
- 11. Scholarly Document Processing • Challenges • Supervised learning is hard: annotation is expensive, requiring domain experts • The text is diverse across fields • How can we improve tools for scholarly document processing across fields? • What training data is readily available? 26/08/2021 11
- 13. Citances in Machine Learning 26/08/2021 13 We use the model from the original BERT paper (Devlin et al. 2019). Cite-worthiness: Is this a citance? Yes Recommendation: What paper should be cited? Devlin et al. (2019) Influence: Was this an influential paper? Yes Intent: What is the purpose of the citation? Method
- 14. Cite-Worthiness Uses 26/08/2021 14 We use the model from the original BERT paper (Devlin et al. 2019). As an auxiliary task in a multi-task setup We use the model from the original BERT paper (Devlin et al. 2019). CITE-WORTHY METHOD
- 15. Cite-Worthiness Uses 26/08/2021 15 We use the model from the original BERT paper (Devlin et al. 2019). As a first step in citation recommendation We use the model from the original BERT paper (Devlin et al. 2019). CITE-WORTHY
- 16. Cite-Worthiness Uses 26/08/2021 16 We use the model from the original BERT paper (Devlin et al. 2019). For assistive document editing We use the model from the original BERT paper (Devlin et al. 2019). CITE-WORTHY
- 17. Cite-Worthiness Datasets • Tend to be small and limited to only a few domains (e.g. Computer Science) • No attention paid to how clean is the data 26/08/2021 17 We use the model from Devlin et al. (2019) as a baseline. e.g. ungrammatical phrases
- 18. CiteWorth: Dataset Curation 26/08/2021 18 1. https://github.com/allenai/s2orc We use the model from the original BERT paper (Devlin et al. 2019). We use the model from the original BERT paper [1]. Parenthetical author/year and bracketed numerical citations only Citations must be at the end of a sentence • We limit citances as follows • Source data: S2ORC1 – millions of extracted scientific documents from Semantic Scholar RQ1: How can a dataset for cite-worthiness detection be automatically curated with low noise?
- 19. CiteWorth: Cleaning the Data 26/08/2021 19 We use the model from the original BERT paper (Devlin et al. 2019). This model uses self-attention and masked language modeling. 1. Extract whole paragraphs – data is curated at the paragraph level 2. Check all the gold citation spans if they are parenthetical author/year or bracketed numerical 3. Check if all citation spans have been extracted for each sentence 4. Check if all citation spans come at the end of a sentence 5. Remove citation spans using gold spans 6. Check if any citation markers are left over (e.g. hanging prepositions/punctuation) RQ1: How can a dataset for cite-worthiness detection be automatically curated with low noise?
- 20. CiteWorth Final Dataset • 1,181,793 sentences • 10 different fields, 20,000+ paragraphs per field • Much cleaner than a naive baseline which only removes citation text based on gold spans 26/08/2021 20 RQ1: How can a dataset for cite-worthiness detection be automatically curated with low noise? Method Sentences Clean (%) Citation Markers Removed (%) Naive Baseline 92.07 92.78 CiteWorth (Ours) 98.90 98.10
- 21. Predicting on Individual Sentences 26/08/2021 21 Pretrained Language Models Transformer Network Logistic Regression Multi-Head Attention Feed- Forward Add & Norm Add & Norm Embedding 2 2. https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270 RQ2: What methods are most effective for automatically detecting cite-worthy sentences? Convolutional Recurrent Net1 1. Michael Färber, Alexander Thiemann, and Adam Ja-towt. 2018b. To Cite, or Not to Cite? DetectingCitation Contexts in Text. InEuropean Conferenceon Information Retrieval, pages 598–603. Springer.
- 22. Predicting on Individual Sentences 26/08/2021 22 Can context improve performance? Method P R F1 Logistic Regression 46.65 64.88 54.28 CRNN 50.87 62.21 55.97 Transformer 47.92 71.59 57.39 BERT 55.04 69.02 61.23 SciBERT 57.03 68.08 62.06 RQ2: What methods are most effective for automatically detecting cite-worthy sentences? * Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, and Sven Dähne. 2016. Investigating the Influence of Noise and Distractors on the Interpretation of Neural Networks. arXiv preprint arXiv:1611.07270.
- 23. Predicting Multiple Sentences at Once 26/08/2021 23 Are there variations across field? RQ2: What methods are most effective for automatically detecting cite-worthy sentences? Longformer* [CLS] !" " !" # [SEP] !# " !# # !# $ [SEP] Pooling Classify Pooling Classify … … Method P R F1 SciBERT 57.03 68.08 62.06 Longformer-Solo 57.21 68.00 62.14 Longformer-Ctx 59.92 77.15 67.45 Δ 5 pts * Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Longformer: The Long-Document Transformer. CoRR, abs/2004.05150.
- 24. Transfer Learning • Pretrain a model and fine tune on 10 tasks (NER, relation extraction, text classification) • Base: Original SciBERT model fine-tuned on downstream tasks • LM: SciBERT with MLM fine-tuning on CiteWorth • Cite: SciBERT fine-tuned on cite-worthiness detection • LMCite: SciBERT with MLM fine-tuning on CiteWorth + fine- tuned on cite-worthiness 26/08/2021 24 RQ4: Can large scale cite-worthiness data be used to perform transfer learning to downstream scientific text tasks?
- 25. Transfer Learning 26/08/2021 25 78,2 78,25 78,3 78,35 78,4 78,45 78,5 78,55 78,6 78,65 78,7 78,75 Average Across Tasks Base LM Cite LMCite The best average performance across tasks is MLM + cite-worthiness fine-tuning RQ4: Can large scale cite-worthiness data be used to perform transfer learning to downstream scientific text tasks?
- 26. Conclusions • We introduce CiteWorth – a large, rigorously cleaned dataset for citation-related tasks • We show that paragraph level context is crucial to perform cite-worthiness detection • We show that the data is diverse with a significant domain effect • We show that cite-worthiness is a highly transferable task for scientific text 26/08/2021 26
- 27. Open Questions • How to improve domain adaptation for scientific text? • What other useful features are there? • Author network • Document level context • Other types of structure (case study: Discourse Structure) • Other tasks using this data e.g. citation recommendation 26/08/2021 27
- 28. Overview of Today’s Talk • Introduction • The Life Cycle of Scientific Research • Part 1: Cite-Worthiness Detection • The CiteWorth dataset • Methods for cite-worthiness detection • Part 2: Exaggeration Detection • Task framing • Semi-supervised learning for exaggeration detection • Conclusion • Future research challenges
- 29. Semi-Supervised Exaggeration Detection of Health Science Press Releases Dustin Wright, Isabelle Augenstein EMNLP 2021 (Main) 29
- 30. Science Communication 26/08/2021 30 Press Release BBC DailyMail The Express Etc...
- 31. Problem 26/08/2021 31 https://www.sciencedaily.com/releases/2021/05/210525101658.htm Yijun Bao, Somayyeh Soltanian-Zadeh, Sina Farsiu, Yiyang Gong. Segmentation of neurons from fluorescence calcium recordings beyond real time. Nature Machine Intelligence, 2021; DOI: 10.1038/s42256-021-00342-x Abstract makes a conditionally causal claim (”potentially enabling”) while the press release makes a direct causal claim
- 32. Our Contributions • Formalisation of the problem of scientific exaggeration detection • Curation of benchmark dataset for scientific exaggeration detection • Semi-supervised method based on Pattern Exploiting Training (PET) to address the task 26/08/2021 32
- 33. Prior Work on Understanding Exaggeration in Science • Manual attempts • Sumner et al. 2014 and Bratton et al. 2019: InSciOut • Manually label 823 pairs of press releases and abstracts • Labels: causal claim strength of conclusions, advice given, independent and dependent variables, etc. • Find that about 33% of press releases contain exaggerated conclusions • Major problem: ”dominant link between academia and the media” are press releases • Automatic attempts • Li et al. 2017, Yu et al. 2019, Yu et al. 2020 • Predict causal claim strength of conclusion sentences in abstract and press release • No clean paired data for evaluation 26/08/2021 33
- 34. Our Work on Exaggeration Detection in Science • The focus of this work is predicting when a press release exaggerates a scientific paper • We focus on predicting this using the primary finding of the paper as written in the abstract and the press release • We build on previous work which focuses on causal claim strength prediction of these primary findings 26/08/2021 34
- 35. Task Formulations and Evaluation Data 26/08/2021 35
- 36. Formal Problem Definition 26/08/2021 36 ! = #$, &$, '$ ( ∈ [0 … -)} Dataset ! Source documents &$ Target documents #$ written about &$ Labels '$, where '$ ∈ 0 0 Downplays 1 Same 2 Exaggerates indicates if #$ exaggerates, downplays, or faithfully represents &$ Learning goal: predict ' given & and #
- 37. Task Formulations • T1 • Entailment-like task to predict exaggeration label • Paired (press release, abstract) data 26/08/2021 37 ℒ"# = % 0 Downplays 1 Same 2 Exaggerates • T2 • Text classification task to predict causal claim strength • Unpaired press releases and abstracts • Final prediction compares strength of paired press release and abstract ℒ": = 0 No Relation 1 Correlational 2 Conditional Causal 3 Direct Causal Label Type Language Cue 0 No Relation 1 Correlational association, associated with, predictor, at high risk of 2 Conditional causal increase, decrease, lead to, effect on, contribute to, result in (Cues indicating doubt: may, might, appear to, probably) 3 Direct causal increase, decrease, lead to, effective on, contribute to, reduce, can Li et al. 2017
- 38. Evaluation Dataset Creation 26/08/2021 38 Start with the 823 labeled pairs from Sumner et al. 2014 and Bratton et al. 2019 (InSciOut) Collect original abstract text from Semantic Scholar Match original conclusion sentences to paraphrased annotations via ROUGE score Manually inspect and discard missing or incorrect abstracts ! Downplays +, < +. Same +, = +. Exaggerates +, > +. Final label: compare annotated claim strength (+, for press release, +. for abstract) Total data: 663 pairs (100 training, 553 test)
- 39. Few-Shot Learning: Multi-Task PET (MT-PET) 26/08/2021 39
- 40. PET (Schick et al. 2020) 26/08/2021 40 Eating chocolate causes happiness ! 0.01 0.21 0.15 '. () 0 1 2 3 Traditional Classifier Eating chocolate causes happiness. The claim strength is [MASK] ℳ 0.01 0.21 0.15 '. () PET m edium estim ated cautious distorted Pattern: transform the input to a cloze-style question Verbalizer: predict tokens from the language model which reflect the data’s labels Large pretrained language model +, +- +. ℳ, ℳ- ℳ. / ! 0 / Soft Labels KL-Divergence Loss (Unlabelled)
- 41. MT-PET 26/08/2021 41 Eating chocolate causes happiness. The claim strength is [MASK] ℳ 0.01 0.21 0.15 '. () m edium estim ated cautious distorted Scientists claim eating chocolate sometimes causes happiness. Reporters claim eating chocolate causes happiness. The reporters claims are [MASK] 0.01 0.05 '. *+ prelim inary identical naive ,- ,. ,- / ℳ/ 0- 1 2- 0- Soft Labels KL-Divergence Loss (Unlabelled) ,. / 2. ,- 3 ℳ3 2- ,. 3 2.
- 43. T1 (Exaggeration Detection) with MT-PET 26/08/2021 43 28,06 33,1 29,05 41,9 39,87 39,12 47,8 47,99 47,35 25 30 35 40 45 50 P R F1 Supervised PET MT-PET Substantial improvements when using PET (10 points) Further improvements with MT-PET (8 points) Demonstrates transfer of knowledge from claim strength prediction to exaggeration prediction
- 44. T2 (Claim Strength Prediction) with MT-PET 26/08/2021 44 49,28 51,07 49,03 55,76 58,58 56,57 56,68 60,13 57,44 45 50 55 60 P R F1 Supervised PET MT-PET 58,2 59,99 58,66 58,53 61,84 60,45 60,09 61,11 45 50 55 60 P R F1 Supervised PET MT-PET MT-PET outperforms PET in both scenarios 200 samples from T2, 100 samples from T1 4500 samples from T2, 100 samples from T1
- 45. T2 (Claim Strength Prediction) with MT-PET 26/08/2021 45 49,28 51,07 49,03 55,76 58,58 56,57 56,68 60,13 57,44 45 50 55 60 P R F1 Supervised PET MT-PET 58,2 59,99 58,66 58,53 61,84 60,45 60,09 61,11 45 50 55 60 P R F1 Supervised PET MT-PET MT-PET with 200 samples approaches supervised performance with 4,500 samples 200 samples from T2, 100 samples from T1 4500 samples from T2, 100 samples from T1
- 46. Error Analysis • All models: • disproportionately get pairs involving direct causal claims incorrect • do best for correlational claims from abstracts and claims from press releases which are correlational or stronger • MT-PET: • helps the most for the most difficult category -- causal claims 26/08/2021 46
- 47. Summary • We formalize the problem of scientific exaggeration detection, providing two task formulations for the problem • We curate a set of benchmark data to evaluate automatic methods for performing the task • We propose MT-PET, a few-shot learning method based on PET, which we demonstrate outperforms strong baselines 26/08/2021 47
- 48. Overview of Today’s Talk • Introduction • The Life Cycle of Scientific Research • Part 1: Cite-Worthiness Detection • The CiteWorth dataset • Methods for cite-worthiness detection • Part 2: Exaggeration Detection • Task framing • Semi-supervised learning for exaggeration detection • Conclusion • Future research challenges
- 49. Wrap-Up 49
- 50. Supporting the Life Cycle of Research 26/08/2021 50 Reviewing Support Citation Analysis Writing Assistance Information Discovery Conducting Experiments Paper Writing Peer Review Research Impact Tracking Information Extraction Summarisa tion Citation Prediction Reviewer Matching Review Score Prediction Citation Prediction Citation Trend Analysis
- 51. Supporting the Life Cycle of Research 26/08/2021 51 Reviewing Support Citation Analysis Writing Assistance Information Discovery Conducting Experiments Paper Writing Peer Review Research Impact Tracking Information Extraction Summarisa tion Citation Prediction Credibility Detection Reviewer Matching Review Score Prediction Citation Prediction Citation Trend Analysis NEW
- 52. Overall Take-Aways • Why scholarly document processing? • Supporting the life cycle of research, from information discovery to research impact tracking • Why credibility detection for scholarly communication? • Detect claims which should be backed up by evidence (cite-worthiness detection) • Detect inconsistencies between primary and secondary sources of information (exaggeration detection)
- 53. Overall Take-Aways • Overarching challenges • Difficult NLP tasks (require understanding of pragmatics) • Domain effects, importance of context pose further challenges • Not well-studied yet • Scarcity of available benchmarks • Many opportunities for future work • Explore more different settings • Gather more datasets • Methods for domain adaptation & few-shot learning • Tools for journalists & authors
- 55. Acknowledgements 55 CopeNLU https://copenlu.github.io/ This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801199. PhD student: Dustin Wright
- 56. Presented Papers Isabelle Augenstein. Determining the Credibility of Science Communication. SDP workshop, 2021. Dustin Wright, Isabelle Augenstein. CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding. ACL Findings, 2021. Dustin Wright, Isabelle Augenstein. Semi-Supervised Exaggeration Detection of Health Science Press Releases. EMNLP, 2021.