Papers by Surya Nepal
Recognising Agreement and Disagreement between Stances with Reason Comparing Networks (P19-1)
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| Challenge: | Existing methods for (dis)agreement detection focus on conversational settings . however, non-dialogic stance-bearing utterances are common in real-world scenarios . |
| Approach: | They propose a reason comparing network to leverage reason information for stance comparison. |
| Outcome: | The proposed method outperforms baselines on a well-known stance corpus. |
Cross-Target Stance Classification with Self-Attention Networks (P18-2)
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| Challenge: | In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. |
| Approach: | They propose a neural model that can generalize classifiers between different targets by finding useful information shared between relevant targets. |
| Outcome: | The proposed model can generalize between relevant targets and find useful information shared between relevant target domains which improves generalization in certain scenarios. |
Assessing Social License to Operate from the Public Discourse on Social Media (2020.coling-industry)
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| Challenge: | Social License to Operate (SLO) is the level of support organisations gain from the public. |
| Approach: | They propose to extract and transform peoples’ stances towards an organisation into SLO levels by performing a chain of three text classification tasks. |
| Outcome: | The proposed system extracts and transforms peoples’ stances towards an organisation into SLO levels. |
Adversarial Attacks Against Automated Fact-Checking: A Survey (2025.emnlp-main)
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Fanzhen Liu, Sharif Abuadbba, Kristen Moore, Surya Nepal, Cecile Paris, Jia Wu, Jian Yang, Quan Z. Sheng
| Challenge: | Existing fact-checking systems are vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs. |
| Approach: | They examine the impact of adversarial attacks on existing AFC systems and examine their impact on existing ones. |
| Outcome: | The findings highlight the need for resilient fact-checking frameworks in limiting misinformation spread and supporting public trust. |