多合成形态学分析中的自举技术
Bootstrapping Techniques for Polysynthetic Morphological Analysis
摘要：
由于一个词中词素槽和组合的数量，多合成语言的词汇量特别大，而且非常稀少。这种复杂性，加上书面数据的普遍缺乏，对自然语言技术的发展提出了挑战。为了应对这一挑战，我们提供了语言学上知情的方法引导神经形态分析仪，并展示其应用Kunwinjku，一个多合成的澳大利亚语言。我们从有限状态传感器生成数据来训练编码器-解码器模型。我们通过将缺失的语言结构“幻觉化”到训练数据中来改进模型，并通过从Zipf分布中恢复来模拟更自然的词素分布。最好的模型解释了测试集中的所有重复实例，总体准确率达到94.7%，比FST基线提高了10个百分点。这个过程证明了从最少的资源引导神经形态分析器的可行性。
英文摘要：
Polysynthetic languages have exceptionally large and sparse vocabularies, thanks to the number of morpheme slots and combinations in a word. This complexity, together with a general scarcity of written data, poses a challenge to the development of natural language technologies. To address this challenge, we offer linguistically-informed approaches for bootstrapping a neural morphological analyzer, and demonstrate its application to Kunwinjku, a polysynthetic Australian language. We generate data from a finite state transducer to train an encoder-decoder model. We improve the model by "hallucinating" missing linguistic structure into the training data, and by resampling from a Zipf distribution to simulate a more natural distribution of morphemes. The best model accounts for all instances of reduplication in the test set and achieves an accuracy of 94.7% overall, a 10 percentage point improvement over the FST baseline. This process demonstrates the feasibility of bootstrapping a neural morph analyzer from minimal resources.
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