论文标题
部分可观测时空混沌系统的无模型预测
Towards Computationally Verifiable Semantic Grounding for Language Models
论文作者
论文摘要
本文提出了一种语言模型（LMS）的语义基础的方法，该方法将LM概念化为有条件的模型生成文本的条件模型，并给定所需的语义消息形式化为一组实体关系三元组。它通过将其输出馈送到语义解析器中，将LM嵌入自动编码器中，该语义解析器的输出与输入消息相同。与使用贪婪搜索生成文本的基线相比，我们演示了两种提高生成文本的流利度和语义准确性的技术：第一种技术示例语义解析器选择的多个候选文本序列。第二个训练语言模型的同时保持语义解析器冻结，以提高自动编码器的语义准确性。我们使用BLEU在英语WebNLG 3.0数据集上进行实验，以测量生成的文本和标准解析指标的流畅性，以衡量语义精确度。我们表明，我们提出的方法在贪婪的搜索基线上有了显着改善。人类评估证实了自动评估实验的结果。
The paper presents an approach to semantic grounding of language models (LMs) that conceptualizes the LM as a conditional model generating text given a desired semantic message formalized as a set of entity-relationship triples. It embeds the LM in an auto-encoder by feeding its output to a semantic parser whose output is in the same representation domain as the input message. Compared to a baseline that generates text using greedy search, we demonstrate two techniques that improve the fluency and semantic accuracy of the generated text: The first technique samples multiple candidate text sequences from which the semantic parser chooses. The second trains the language model while keeping the semantic parser frozen to improve the semantic accuracy of the auto-encoder. We carry out experiments on the English WebNLG 3.0 data set, using BLEU to measure the fluency of generated text and standard parsing metrics to measure semantic accuracy. We show that our proposed approaches significantly improve on the greedy search baseline. Human evaluation corroborates the results of the automatic evaluation experiments.
