Help! Need Advice on Identifying Advice
This work addresses the need for automated advice identification to improve efficiency in online advice-seeking and natural language generation, but it is incremental as it builds on existing language models.
The paper tackles the problem of identifying advice in online forums by creating an annotated dataset from Reddit and showing that pre-trained language models outperform rule-based systems, though the task remains challenging.
Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly. Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increase the efficiency of advice-seeking online, as well as advice-giving in natural language generation systems. We present a dataset in English from two Reddit advice forums - r/AskParents and r/needadvice - annotated for whether sentences in posts contain advice or not. Our analysis reveals rich linguistic phenomena in advice discourse. We present preliminary models showing that while pre-trained language models are able to capture advice better than rule-based systems, advice identification is challenging, and we identify directions for future research. Comments: To be presented at EMNLP 2020.