Abstract
Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its sense).
开放文本语义分析器被设计为通过推断相应的意义表示(MR -其意义的正式表示)来解释自然语言中的任何语句。
Unfortunately, large scale systems cannot be easily machine-learned due to a lack of directly supervised data.
不幸的是，由于缺乏直接监督的数据，大型系统不容易进行机器学习。
We propose a method that learns to assign MRs to a wide range of text (using a dictionary of more than 70,000 words mapped to more than 40,000 entities) thanks to a training scheme that combines learning from knowledge bases (e.g. WordNet) with learning from raw text.
我们提出了一种方法，通过结合从知识库(如WordNet)学习和从原始文本学习的培训计划，学习如何将MRs分配到广泛的文本中(使用映射到超过40,000个实体的超过70,000个单词的字典)。
The model jointly learns representations of words, entities and MRs via a multi-task training process operating on these diverse sources of data.
该模型通过在这些不同数据源上运行的多任务训练过程，联合学习单词、实体和MRs的表示形式。
Hence, the system ends up providing methods for knowledge acquisition and word sense disambiguation within the context of semantic parsing in a single elegant framework.
因此，该系统最终在一个简洁的框架中提供了知识获取和词义消歧的方法。
Experiments on these various tasks indicate the promise of the approach.
对这些不同任务的实验表明了该方法的可行性。
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