Publication: MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching
MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching
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Mishra, R., Yadav, P., Calizzano, R., & Leippold, M. (2020, December 17). MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching. International Conference on Machine Learning and Applications (ICMLA) 2020, Miami.
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Measuring congruence between two texts has several useful applications, such as detecting the prevalent deceptive and misleading news headlines on the web. Many works have proposed machine learning based solutions such as text similarity between the headline and body text to detect the incongruence. Text similarity based methods fail to perform well due to different inherent challenges such as relative length mismatch between the news headline and its body content and non-overlapping vocabulary. On the other hand, more recent works th
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Mishra, R., Yadav, P., Calizzano, R., & Leippold, M. (2020, December 17). MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching. International Conference on Machine Learning and Applications (ICMLA) 2020, Miami.
