Text Intimacy Analysis using Ensembles of Multilingual Transformers
This work addresses the need for NLP systems to understand intimacy in human communication, but it is incremental as it builds on existing multilingual transformer methods for a specific shared task.
The paper tackled the problem of predicting text intimacy levels across multiple languages, achieving the best performance by using an ensemble of multilingual and language-specific models.
Intimacy estimation of a given text has recently gained importance due to the increase in direct interaction of NLP systems with humans. Intimacy is an important aspect of natural language and has a substantial impact on our everyday communication. Thus the level of intimacy can provide us with deeper insights and richer semantics of conversations. In this paper, we present our work on the SemEval shared task 9 on predicting the level of intimacy for the given text. The dataset consists of tweets in ten languages, out of which only six are available in the training dataset. We conduct several experiments and show that an ensemble of multilingual models along with a language-specific monolingual model has the best performance. We also evaluate other data augmentation methods such as translation and present the results. Lastly, we study the results thoroughly and present some noteworthy insights into this problem.