Sequence-to-sequence learning with neural networks has become the de facto standard for sequence prediction tasks. This approach typically models the local distribution over the next word with a powerful neural network that can condition on arbitrary context. While flexible and performant, these models often require large datasets for training and can fail spectacularly on benchmarks designed to test for compositional generalization. This work explores an alternative, hierarchical approach to sequence-to-sequence learning with quasi-synchronous grammars, where each node in the target tree is transduced by a node in the source tree. Both the source and target trees are treated as latent and induced during training. We develop a neural parameterization of the grammar which enables parameter sharing over the combinatorial space of derivation rules without the need for manual feature engineering. We apply this latent neural grammar to various domains -- a diagnostic language navigation task designed to test for compositional generalization (SCAN), style transfer, and small-scale machine translation -- and find that it performs respectably compared to standard baselines.
翻译:神经网络的序列到序列学习已成为测序任务的实际标准。 这种方法通常以一个强大的神经网络为下个词的本地分布模型, 并且有一个强大的神经网络, 可以随任意环境而定。 虽然这些模型具有灵活性和性能性, 但是这些模型往往需要大量的数据集用于培训, 并且对于用来测试构成性一般化的基准则可能大失所望。 这项工作探索了一种替代的、 等级分级的方法来进行序列到序列学习, 使用准同步语法学习, 目标树的每个节点都通过源树中的节点转换。 源树和目标树在培训期间都被视为潜在和诱导的。 我们开发了语法的神经参数参数参数化, 使衍生规则的组合空间得以共享, 而不需要人工特征工程。 我们将这一潜在的神经语法图应用于多个领域 -- 一种诊断语言导航任务, 旨在测试构成性一般化( SCAN)、 风格转换和小型机器翻译, 并发现它与标准基线相对, 并发现它运行得体化 。