- Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., & Potts, C. (2013). A computational approach to politeness with application to social factors.
Human speech is a interesting in a sense that, not only is it used to convey information from one person to another but it contains other information that helps us understand dynamics between people and so forth. Such encodings are implicitly imbibed through social behaviors that one is surrounded by. [1]
In this paper, the author understands one of these encodings, politiness through computational models. In order to build said computational model the author first makes a dataset from two sources wikipedia (feature development) and stackoverflow (feature transfer). The annotations are done by Amozon Mechanical Turkers. A classifier is then built which uses domain independent lexical and syntactic features extracted from the above above dataset to achieve near human levels of accuracy on classifying a text as polite or impolite. This model is then used to identify/reaffirm the following:
- Politeness decreases as one moves the social power dynamic food chain
- Politeness variation by gender and community
One of the interesting points that I thought one could work on after reading the paper, is to see how a language could affect the measure of politeness? One could see how different cultures which therein affecting the language could have an impact. This could depend upon the structure of the society and so forth. [2] [3]
One could also see how if the model described above used in domains where the vocabulary is different let’s say a website where the internet slang is rampant like 4chan would fail to work.
The above two instances can be looked as how politeness understood from one domain could not work in another where the underlying factors that determine politiness is different.
Given the dataset and its content, I belive what the author has built a computational model for is likability rather than it’s superset, politeness which is more encomposing than the former.
The author has used SVM to learn from features extracted separately from sociolinguistic theories of politeness. One could see how a newer deep learning based method could be used to extract these features to jointly learn the phenomenon of politeness [4].
[1] https://hbr.org/1995/09/the-power-of-talk-who-gets-heard-and-why
[2] Haugh, Michael. “Revisiting the conceptualisation of politeness in English and Japanese.” Multilingua 23.1/2 (2004): 85-110.
[3] Nelson, Emiko Tajikara. “The expression of politeness in Japan: intercultural implications for Americans.” (1987).
[4] Aubakirova, Malika, and Mohit Bansal. “Interpreting neural networks to improve politeness comprehension.” arXiv preprint arXiv:1610.02683 (2016).
