Weakly Supervised Semantic Parsing with Abstract Examples
Omer Goldman, Veronica Latcinnik, Udi Naveh, Amir Globerson, Jonathan Berant Tel-Aviv University Authors equally contributed to this work.
Abstract
Training semantic parsers from weak supervision (denotations) rather than strong supervision (programs) complicates training in two ways. First, a large search space of potential programs needs to be explored at training time to find a correct program. Second, spurious programs that accidentally lead to a correct denotation add noise to training. In this work we propose that in closed worlds with clear semantic types, one can substantially alleviate these problems by utilizing an abstract representation, where tokens in both the language utterance and program are lifted to an abstract form. We show that these abstractions can be defined with a handful of lexical rules and that they result in sharing between different examples that alleviates the difficulties in training. To test our approach, we develop the first semantic parser for CNLVR, a challenging visual reasoning dataset, where the search space is large and overcoming spuriousness is critical, because denotations are either True or False, and thus random programs are likely to lead to a correct denotation. Our method substantially improves performance, and reaches 82.5% accuracy, a 14.7% absolute accuracy improvement compare
中文速览
弱监督语义解析（weak supervision semantic parsing）训练时面临两大难题：搜索空间爆炸和"虚假程序"（spurious programs）干扰——程序碰巧给出正确答案却逻辑错误。针对这两个问题，研究者提出用"抽象表示"（abstract representation）来共享不同样本间的结构信息：通过少量词汇规则，将自然语言表达和程序中的词元统一提升到抽象层次，使表面形式不同但结构相似的例子能够互相借力。他们将这一方法应用于视觉推理数据集 CNLVR，该数据集只有 True/False 两种答案，虚假程序问题尤为突出，此前从未有人为其训练出语义解析器。实验结果显示，新方法达到 82.5% 的准确率，比此前最优结果提升了 14.7 个百分点，证明抽象表示是解决弱监督语义解析核心痛点的有效途径。
原文 arXiv:1711.05240;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1711.05240v5