Not a Simple Yes or No: Uncertainty in Indirect Answers
Not a Simple Yes or No: Uncertainty in Indirect Answers
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不是简单的是或否：间接答案的不确定性
DOI：
10.3115/1708376.1708396
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发表时间：
2009
影响因子：
3.4
通讯作者：
Christopher Potts
中科院分区：
文献类型：
--
作者：
M. Marneffe;Scott Grimm;Christopher Potts
用逻辑来解释间接言语行为有着悠久的历史。然而，经典的逻辑推理无法处理在话语中形成这种言语行为的不同的、冲突的、不确定的证据的组合。我们建议通过将逻辑推理与概率方法相结合来解决这个问题。我们专注于回答具有以下属性的极性问题：它们既不是是也不是，但它们传达的信息可以用来推断这样一个答案，具有一定程度的信心，虽然往往没有足够的信心来算作解决。我们提出了一个新的语料库研究和相关的类型学，旨在将这些反应在更广泛的类间接问答对（IQAP）。然后，我们使用马尔可夫逻辑网络，它结合联合收割机一阶逻辑与概率的不同类型的IQAP模型，强调这种方法使我们能够建模的方式，话语和预期的含义的上下文推理的不确定性。
There is a long history of using logic to model the interpretation of indirect speech acts. Classical logical inference, however, is unable to deal with the combinations of disparate, conflicting, uncertain evidence that shape such speech acts in discourse. We propose to address this by combining logical inference with probabilistic methods. We focus on responses to polar questions with the following property: they are neither yes nor no, but they convey information that can be used to infer such an answer with some degree of confidence, though often not with enough confidence to count as resolving. We present a novel corpus study and associated typology that aims to situate these responses in the broader class of indirect question--answer pairs (IQAPs). We then model the different types of IQAPs using Markov logic networks, which combine first-order logic with probabilities, emphasizing the ways in which this approach allows us to model inferential uncertainty about both the context of utterance and intended meanings.