Flexible Generation of Natural Language Deductions
Flexible Generation of Natural Language Deductions
复制标题
灵活生成自然语言推论
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Greg Durrett
中科院分区：
文献类型：
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作者：
Kaj Bostrom;Xinyu Zhao;Swarat Chaudhuri;Greg Durrett
一个可解释的开放领域推理系统需要以透明的形式表达其推理过程。自然语言是一种有吸引力的表示方法-它既具有高度的表达性，又易于人类理解。然而，以逻辑上一致的方式操纵自然语言语句是困难的：模型必须在保持精确的同时科普含义表达方式的变化。在本文中，我们描述了ParaPattern，一种用于构建模型的方法，该模型可以在没有直接人类监督的情况下从不同的自然语言输入中生成演绎推理。我们训练基于BART的模型（刘易斯等人，2020）以生成将特定逻辑运算应用于一个或多个前提语句的结果。至关重要的是，我们开发了一个很大程度上自动化的管道，用于从维基百科构建合适的训练示例。我们使用来自QASC的域外句子成分来评估我们的模型（Khot et al.，2020）和EntailmentBank（Dalvi et al.，2021）数据集以及有针对性的扰动集。我们的研究结果表明，我们的模型比基线系统更准确和灵活。ParaPattern在不使用任何域内训练数据的情况下，对来自EntailmentBank的“替换”操作的示例实现了85%的有效性，与为EntailmentBank微调的模型的性能相匹配。我们的方法的完整源代码是公开的。
An interpretable system for open-domain reasoning needs to express its reasoning process in a transparent form. Natural language is an attractive representation for this purpose — it is both highly expressive and easy for humans to understand. However, manipulating natural language statements in logically consistent ways is hard: models must cope with variation in how meaning is expressed while remaining precise. In this paper, we describe ParaPattern, a method for building models to generate deductive inferences from diverse natural language inputs without direct human supervision. We train BART-based models (Lewis et al., 2020) to generate the result of applying a particular logical operation to one or more premise statements. Crucially, we develop a largely automated pipeline for constructing suitable training examples from Wikipedia. We evaluate our models using out-of-domain sentence compositions from the QASC (Khot et al., 2020) and EntailmentBank (Dalvi et al., 2021) datasets as well as targeted perturbation sets. Our results show that our models are substantially more accurate and flexible than baseline systems. ParaPattern achieves 85% validity on examples of the ‘substitution’ operation from EntailmentBank without the use of any in-domain training data, matching the performance of a model fine-tuned for EntailmentBank. The full source code for our method is publicly available.
DOI：
10.18653/v1/n19-1405
发表时间：
2019
期刊：
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影响因子：
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作者：
Jifan Chen;Greg Durrett
通讯作者：
Jifan Chen;Greg Durrett