Rational integration of noisy evidence and prior semantic expectations in sentence interpretation

判决 计算机科学 句子处理 话语 理解力 自然语言处理 意义(存在) 推论 口译(哲学) 任务(项目管理) 人工智能 认知心理学 语言学 心理学 哲学 管理 经济 心理治疗师 程序设计语言
作者
Edward Gibson,Leon Bergen,Steven T. Piantadosi
出处
期刊:Proceedings of the National Academy of Sciences of the United States of America [National Academy of Sciences]
卷期号:110 (20): 8051-8056 被引量:347
标识
DOI:10.1073/pnas.1216438110
摘要

Sentence processing theories typically assume that the input to our language processing mechanisms is an error-free sequence of words. However, this assumption is an oversimplification because noise is present in typical language use (for instance, due to a noisy environment, producer errors, or perceiver errors). A complete theory of human sentence comprehension therefore needs to explain how humans understand language given imperfect input. Indeed, like many cognitive systems, language processing mechanisms may even be “well designed”–in this case for the task of recovering intended meaning from noisy utterances. In particular, comprehension mechanisms may be sensitive to the types of information that an idealized statistical comprehender would be sensitive to. Here, we evaluate four predictions about such a rational (Bayesian) noisy-channel language comprehender in a sentence comprehension task: ( i ) semantic cues should pull sentence interpretation towards plausible meanings, especially if the wording of the more plausible meaning is close to the observed utterance in terms of the number of edits; ( ii ) this process should asymmetrically treat insertions and deletions due to the Bayesian “size principle”; such nonliteral interpretation of sentences should ( iii ) increase with the perceived noise rate of the communicative situation and ( iv ) decrease if semantically anomalous meanings are more likely to be communicated. These predictions are borne out, strongly suggesting that human language relies on rational statistical inference over a noisy channel.

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