论文标题
测量神经SEQ2SEQ语义解析器中的对齐偏差
Measuring Alignment Bias in Neural Seq2Seq Semantic Parsers
论文作者
论文摘要
在深入学习之前，语义解析社区一直对理解和建模自然语言句子及其相应含义表示之间可能的单词一致性范围。序列到序列模型改变了研究领域，这表明我们不再需要担心对齐，因为它们可以通过注意机制自动学习。最近，研究人员开始质疑这种前提。在这项工作中，我们研究了SEQ2SEQ模型是否可以处理简单和复杂的对齐。为了回答这个问题，我们通过对齐注释来增强流行的地理语义解析数据集并创建地理位置一致。然后，我们在示例上研究标准SEQ2SEQ模型的性能，这些示例可以单调地对齐，而这些示例与需要更复杂的比对的示例。我们的实证研究表明，在单调比对方面的性能明显更好。
Prior to deep learning the semantic parsing community has been interested in understanding and modeling the range of possible word alignments between natural language sentences and their corresponding meaning representations. Sequence-to-sequence models changed the research landscape suggesting that we no longer need to worry about alignments since they can be learned automatically by means of an attention mechanism. More recently, researchers have started to question such premise. In this work we investigate whether seq2seq models can handle both simple and complex alignments. To answer this question we augment the popular Geo semantic parsing dataset with alignment annotations and create Geo-Aligned. We then study the performance of standard seq2seq models on the examples that can be aligned monotonically versus examples that require more complex alignments. Our empirical study shows that performance is significantly better over monotonic alignments.
