Semantically Informed Slang Interpretation
This work addresses the challenge of automated interpretation and translation of informal language for NLP systems, with incremental improvements over existing context-based methods.
The paper tackled the problem of interpreting slang in natural language processing by proposing a semantically informed framework that jointly considers context and semantic extensions, achieving state-of-the-art accuracy on large-scale slang dictionaries and demonstrating effectiveness in zero-shot and few-shot scenarios.
Slang is a predominant form of informal language making flexible and extended use of words that is notoriously hard for natural language processing systems to interpret. Existing approaches to slang interpretation tend to rely on context but ignore semantic extensions common in slang word usage. We propose a semantically informed slang interpretation (SSI) framework that considers jointly the contextual and semantic appropriateness of a candidate interpretation for a query slang. We perform rigorous evaluation on two large-scale online slang dictionaries and show that our approach not only achieves state-of-the-art accuracy for slang interpretation in English, but also does so in zero-shot and few-shot scenarios where training data is sparse. Furthermore, we show how the same framework can be applied to enhancing machine translation of slang from English to other languages. Our work creates opportunities for the automated interpretation and translation of informal language.