论文标题
使用有序神经元的潜在树学习：它会产生什么解析？
Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?
论文作者
论文摘要
最近的潜在树学习模型可以学习选区解析，而无需接触人类注销的树结构。这样的模型是On-LSTM（Shen等，2019），该模型接受了语言建模的培训，并且在无监督解析方面具有近乎状态的表现。为了更好地了解模型的性能和一致性以及其生成的解析与金标准PTB解析的不同，我们通过不同的重新启动复制模型并检查其分析。我们发现（1）模型在不同重新启动的跨不同重新启动方面具有相当一致的解析行为，（2）模型与复杂名词短语的内部结构进行斗争，（3）模型倾向于高估动词前动词之前的分离点的高度。我们推测，除了单向语言建模以外，还可以通过采用不同的培训任务来解决这两个问题。
Recent latent tree learning models can learn constituency parsing without any exposure to human-annotated tree structures. One such model is ON-LSTM (Shen et al., 2019), which is trained on language modelling and has near-state-of-the-art performance on unsupervised parsing. In order to better understand the performance and consistency of the model as well as how the parses it generates are different from gold-standard PTB parses, we replicate the model with different restarts and examine their parses. We find that (1) the model has reasonably consistent parsing behaviors across different restarts, (2) the model struggles with the internal structures of complex noun phrases, (3) the model has a tendency to overestimate the height of the split points right before verbs. We speculate that both problems could potentially be solved by adopting a different training task other than unidirectional language modelling.
