Summary of Linguistically Conditioned Semantic Textual Similarity, by Jingxuan Tu and Keer Xu and Liulu Yue and Bingyang Ye and Kyeongmin Rim and James Pustejovsky
Linguistically Conditioned Semantic Textual Similarity
by Jingxuan Tu, Keer Xu, Liulu Yue, Bingyang Ye, Kyeongmin Rim, James Pustejovsky
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Conditional Semantic Textual Similarity (C-STS) task measures sentence similarity conditioned on specific aspects, aiming to reduce ambiguity. Despite its popularity, the current C-STS dataset faces issues hindering proper evaluation, including annotator discrepancies and unclear task definitions. This paper reannotates the validation set, identifying 55% of instances with errors. A new automatic error identification pipeline is developed, achieving an F1 score over 80%. The study also proposes a method improving performance on C-STS data by training models with generated answers. Finally, it discusses conditionality annotation based on Typed-Feature Structure (TFS) entity types, providing a linguistic foundation for constructing C-STS data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in natural language processing called Conditional Semantic Textual Similarity. It’s like trying to measure how similar two sentences are, but with a special twist: you have to consider specific details or “conditions” that make the sentences different. Right now, there’s an issue with the data used to test this task, and the paper fixes it by rechecking the answers. The study also comes up with new ways to use computers to solve this problem more accurately. |
Keywords
* Artificial intelligence * F1 score * Natural language processing
