Help! Need Advice on Identifying Advice
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866913037298958336 |
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| author | Govindarajan, Venkata Subrahmanyan Chen, Benjamin T Warholic, Rebecca Erk, Katrin Li, Junyi Jessy |
| author_facet | Govindarajan, Venkata Subrahmanyan Chen, Benjamin T Warholic, Rebecca Erk, Katrin Li, Junyi Jessy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2010_02494 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Help! Need Advice on Identifying Advice Govindarajan, Venkata Subrahmanyan Chen, Benjamin T Warholic, Rebecca Erk, Katrin Li, Junyi Jessy Computation and Language 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. |
| title | Help! Need Advice on Identifying Advice |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2010.02494 |