Mutual Implication as a Measure of Textual Equivalence


  • Animesh Nighojkar University of South Florida
  • John Licato University of South Florida



Semantic Textual Similarity (STS) and paraphrase de- tection are two NLP tasks that have a high focus on the meaning of sentences, and current research in both re- lies heavily on comparing fragments of text. Little to no work has been done in studying inference-centric ap- proaches to solve these tasks. We study the relation be- tween existing work and what we call mutual implica- tion (MI), a binary relationship between two sentences that holds when they textually entail each other. MI thus shifts the focus of STS and paraphrase detection to un- derstanding the meaning of a sentence in terms of its in- ferential properties. We study the comparison between MI, paraphrasing, and STS work. We then argue that MI should be considered a complementary evaluation met- ric for advancing work in areas as diverse as machine translation, natural language inference, etc. Finally, we study the limitations of MI and discuss possibilities for overcoming them.

Author Biography

John Licato, University of South Florida

Dr. John Licato is an assistant professor at the university of south florida, and director of the Advancing Machine and Human Reasoning (AMHR) Lab.




How to Cite

Nighojkar, A., & Licato, J. (2021). Mutual Implication as a Measure of Textual Equivalence. The International FLAIRS Conference Proceedings, 34.



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