What do linguistic models reveal about the emotions associated with words?In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, and finally a self-supervised method of extracting emotional associations from pretrained vectors and models.Overall, we find that linguistic models carry substantial potential for inducing fine-grained emotion intensity scores, showing a far higher correlation with human ground truth ratings than state-of-the-art emotion lexicons based on labeled data.

Abstract Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of language understanding tasks—reading comprehension, textual entailment, and so on. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5k new instances across 6 distinct NLU tasks. Additionally, we present the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.1

Affective analysis of textual data is instrumental in understanding human communication in the modern era of social media. A number of resources have been proposed in attempts to characterize the emotions tied to words in a text. In this work, we show that we can obtain a database that goes beyond the common binary scores for emotion classification provided by past work. Instead, we harness the power of Big Data by using neural vector space models trained with large-scale supervision from co-occurrence patterns. We modify the vector space to better account for emotional associations, which then enables us to induce AffectVec, a new emotion database providing graded emotion intensity scores for English language words with regard to a fine-grained inventory of over 200 different emotion categories. Our experiments show that AffectVec outperforms existing emotion lexicons by substantial margins in intrinsic evaluations as well as for affective text classification.