Summary of Computational Politeness in Natural Language Processing: a Survey, by Priyanshu Priya et al.
Computational Politeness in Natural Language Processing: A Survey
by Priyanshu Priya, Mauajama Firdaus, Asif Ekbal
First submitted to arxiv on: 28 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 A novel computational approach to politeness is explored in this paper, aiming to automatically predict and generate politeness in text. This research is significant for conversational analysis, given the omnipresence and challenges of politeness in interactions. The study reviews four milestones in computational politeness: supervised and weakly-supervised feature extraction, contextual consideration beyond the target text, exploration of politeness across different social factors, and investigation of politeness’s relationship with various sociolinguistic cues. The paper discusses datasets, approaches, trends, and issues in computational politeness research, presenting representative performance values and future work directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Computational politeness aims to automatically predict and generate politeness in text. This is important because politeness plays a big role in how we communicate with each other. The study looks at four key moments in this area of research: using features to identify politeness, considering what’s happening outside the main conversation, looking at how politeness changes depending on social factors, and understanding how politeness relates to other language cues. The paper also talks about the datasets and methods used, as well as some challenges and future ideas. |
Keywords
» Artificial intelligence » Feature extraction » Supervised
