Mutual Implication as a Measure of Textual Equivalence
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 t...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2021-04-01
|
| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/128519 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2334-0754 2334-0762 |