# DESYR: Definition and Syntactic Representation Based Claim Detection on the Web

arXiv: https://arxiv.org/abs/2108.08759 · HTML: https://mlsanity.com/paper/2108.08759/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]](https://mlsanity.com/paper/1806.01264/) (2018)
- [What is the Essence of a Claim? Cross-Domain Claim Identification](https://mlsanity.com/paper/1704.07203/) (2017)
- [IMHO Fine-Tuning Improves Claim Detection](https://mlsanity.com/paper/1905.07000/) (2019)
- [CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media](https://mlsanity.com/paper/2001.08546/) (2020)
- [LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content](https://mlsanity.com/paper/2101.11891/) (2021)
- [Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based models](https://mlsanity.com/paper/2009.02431/) (2020)
- [Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models](https://mlsanity.com/paper/2009.02931/) (2020)
- [On the Role of Images for Analyzing Claims in Social Media](https://mlsanity.com/paper/2103.09602/) (2021)

## Built on by (most cited first, 7 of 7)

- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter](https://mlsanity.com/paper/2210.04710/) (2022)
- [From Chaos to Clarity: Claim Normalization to Empower Fact-Checking](https://mlsanity.com/paper/2310.14338/) (2023)
- [Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media](https://mlsanity.com/paper/2310.18205/) (2023)
- [Overview of the CLAIMSCAN-2023: Uncovering Truth in Social Media through Claim Detection and Identification of Claim Spans](https://mlsanity.com/paper/2310.19267/) (2023)
- [Leveraging Social Discourse to Measure Check-worthiness of Claims for Fact-checking](https://mlsanity.com/paper/2309.09274/) (2023)
- [The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection](https://mlsanity.com/paper/2509.15896/) (2025)

## Abstract (arXiv)

The formulation of a claim rests at the core of argument mining. To demarcate between a claim and a non-claim is arduous for both humans and machines, owing to latent linguistic variance between the two and the inadequacy of extensive definition-based formalization. Furthermore, the increase in the usage of online social media has resulted in an explosion of unsolicited information on the web presented as informal text. To account for the aforementioned, in this paper, we proposed DESYR. It is a framework that intends on annulling the said issues for informal web-based text by leveraging a combination of hierarchical representation learning (dependency-inspired Poincare embedding), definition-based alignment, and feature projection. We do away with fine-tuning computer-heavy language models in favor of fabricating a more domain-centric but lighter approach. Experimental results indicate that DESYR builds upon the state-of-the-art system across four benchmark claim datasets, most of which were constructed with informal texts. We see an increase of 3 claim-F1 points on the LESA-Twitter dataset, an increase of 1 claim-F1 point and 9 macro-F1 points on the Online Comments(OC) dataset, an increase of 24 claim-F1 points and 17 macro-F1 points on the Web Discourse(WD) dataset, and an increase of 8 claim-F1 points and 5 macro-F1 points on the Micro Texts(MT) dataset. We also perform an extensive analysis of the results. We make a 100-D pre-trained version of our Poincare-variant along with the source code.