Unsupervised Prediction of Acceptability Judgements
Unsupervised Prediction of Acceptability Judgements
复制标题
可接受性判断的无监督预测
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Shalom Lappin
中科院分区：
文献类型：
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作者：
Jey Han Lau;Alexander Clark;Shalom Lappin
本文提出了对说话人可接受性判断进行无监督预测的任务。我们使用了一个由英国国家语料库（BNC）生成的测试集，其中包含语法句子和包含往返机器翻译引入的各种句法缺陷的句子。通过众包对该集合进行注释，以便进行可接受性判断。我们在原始的BNC上训练了各种无监督语言模型，并对它们进行了测试，看看它们能在多大程度上预测平均说话者在测试集中的判断。为了将概率映射到可接受性，我们尝试了几个归一化函数来抵消句子长度和词频的影响。我们发现了令人鼓舞的结果，无监督模型预测了两个不同数据集的可接受性。我们的方法可高度移植到其他领域和语言，并且该方法对语言知识的表示和获取具有潜在的影响。
In this paper we present the task of unsupervised prediction of speakers’ acceptability judgements. We use a test set generated from the British National Corpus (BNC) containing both grammatical sentences and sentences containing a variety of syntactic infelicities introduced by round trip machine translation. This set was annotated for acceptability judgements through crowd sourcing. We trained a variety of unsupervised language models on the original BNC, and tested them to see the extent to which they could predict mean speakers’ judgements on the test set. To map probability to acceptability, we experimented with several normalisation functions to neutralise the effects of sentence length and word frequencies. We found encouraging results with the unsupervised models predicting acceptability across two different datasets. Our methodology is highly portable to other domains and languages, and the approach has potential implications for the representation and the acquisition of linguistic knowledge.
DOI：
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发表时间：
2014
期刊：
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影响因子：
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作者：
Jey Han Lau, Alexander Clark,;Shalom Lappin
通讯作者：
Shalom Lappin