利用Newswire树库解析带有参数置乱的会话数据
Leveraging Newswire Treebanks for Parsing Conversational Data with Argument Scrambling
摘要：
我们调查的问题，解析会话数据的形态丰富的语言，如印地语的参数扰频频繁发生。我们评估了一个国家的最先进的非线性转换为基础的解析系统，一个新的数据集包含506个依赖树的句子从宝莱坞（印地语）电影剧本和Twitter帖子的印地语单语扬声器。我们表明，依赖解析器训练的新闻专线树库是强烈偏向于规范的结构和降低时，应用于会话数据。受转换生成语法的启发，我们通过从树库中现有的（内核）结构生成一个子句的所有理论上可能的替代词序来减轻采样偏差。在规范和转换结构上训练我们的解析器，与基线新闻解析器相比，会话数据的性能提高了约9% LAS。
英文摘要：
We investigate the problem of parsing conversational data of morphologically-rich languages such as Hindi where argument scrambling occurs frequently. We evaluate a state-of-the-art non-linear transition-based parsing system on a new dataset containing 506 dependency trees for sentences from Bollywood (Hindi) movie scripts and Twitter posts of Hindi monolingual speakers. We show that a dependency parser trained on a newswire treebank is strongly biased towards the canonical structures and degrades when applied to conversational data. Inspired by Transformational Generative Grammar, we mitigate the sampling bias by generating all theoretically possible alternative word orders of a clause from the existing (kernel) structures in the treebank. Training our parser on canonical and transformed structures improves performance on conversational data by around 9% LAS over the baseline newswire parser.
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