Perspective-getting (i.e., the effort to obtain information about the other person’s perspective) can lead to more accurate interpersonal understanding. In this paper, we develop an approach to measure perspective-getting and apply it to English Wikipedia discussions. First, we develop a codebook based on perspective-getting theory to operationalize perspective-getting into two categories: asking questions about and attending the other’s perspective. Second, we use the codebook to annotate perspective-getting in Wikipedia discussion pages. Third, we fine-tune a RoBERTa model that achieves an average F-1 score of 0.76 on the two perspective-getting categories. Last, we test whether perspective-getting is associated with discussion outcomes. Perspective-getting was not higher in non-escalated discussions. However, discussions starting with a post attending the other’s perspective are followed by responses that are more likely to also attend the other’s perspective. Future research may use our model to study the influence of perspective-getting on the dynamics and outcomes of online discussions.

Linguistic style is an integral component of language. Recent advances in the development of style representations have increasingly used training objectives from authorship verification (AV)”:” Do two texts have the same author? The assumption underlying the AV training task (same author approximates same writing style) enables self-supervised and, thus, extensive training. However, a good performance on the AV task does not ensure good “general-purpose” style representations. For example, as the same author might typically write about certain topics, representations trained on AV might also encode content information instead of style alone. We introduce a variation of the AV training task that controls for content using conversation or domain labels. We evaluate whether known style dimensions are represented and preferred over content information through an original variation to the recently proposed STEL framework. We find that representations trained by controlling for conversation are better than representations trained with domain or no content control at representing style independent from content.

Style is an integral part of natural language. However, evaluation methods for style measures are rare, often task-specific and usually do not control for content. We propose the modular, fine-grained and content-controlled similarity-based STyle EvaLuation framework (STEL) to test the performance of any model that can compare two sentences on style. We illustrate STEL with two general dimensions of style (formal/informal and simple/complex) as well as two specific characteristics of style (contrac’tion and numb3r substitution). We find that BERT-based methods outperform simple versions of commonly used style measures like 3-grams, punctuation frequency and LIWC-based approaches. We invite the addition of further tasks and task instances to STEL and hope to facilitate the improvement of style-sensitive measures.