Validation of Facts Against Textual Sources

Vamsi Krishna Pendyala, Simran Sinha, Satya Prakash, Shriya Reddy, Anupam Jamatia

Abstract
In today’s digital world of information, a fact verification system to disprove assertions made in speech, print media or online content is the need of the hour. We propose a system which would verify a claim against a source and classify the claim to be true, false, out-of-context or an inappropriate claim with respect to the textual source provided to the system. A true label is used if the claim is true, false if it is false, if the claim has no relation with the source then it is classified as out-of-context and if the claim cannot be verified at all then it is classified as inappropriate. This would help us to verify a claim or a fact as well as know about the source or our knowledge base against which we are trying to verify our facts. We used a two-step approach to achieve our goal. At first, we retrieved evidence related to the claims from the textual source using the Term Frequency-Inverse Document Frequency(TF-IDF) vectors. Later we classified the claim-evidence pairs as true, false, inappropriate and out of context using a modified version of textual entailment module. Textual entailment module calculates the probability of each sentence supporting the claim, contradicting the claim or not providing any relevant information using Bi-LSTM network to assess the veracity of the claim. The accuracy of the best performing system is 64.49%
Anthology ID:
R19-1104
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
895–903
Language:
URL:
https://aclanthology.org/R19-1104
DOI:
10.26615/978-954-452-056-4_104
Bibkey:
Cite (ACL):
Vamsi Krishna Pendyala, Simran Sinha, Satya Prakash, Shriya Reddy, and Anupam Jamatia. 2019. Validation of Facts Against Textual Sources. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 895–903, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Validation of Facts Against Textual Sources (Pendyala et al., RANLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/R19-1104.pdf
PDF Search