Modeling Blame to Avoid Positive Face Threats in Natural Language Generation

作者: Gordon Briggs , Matthias Scheutz

DOI: 10.3115/V1/W14-5001

关键词:

摘要: Prior approaches to politeness modulation in natural language generation (NLG) often focus on manipulating factors such as the directness of requests that pertain preserving autonomy addressee (negative face threats), but do not have a systematic way understanding potential impoliteness from inadvertently critical or blame-oriented communications (positive threats). In this paper, we discuss ongoing work integrate computational model blame prevent inappropriate threats positive face.

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