Neural Boxer at the IWCS Shared Task on DRS Parsing

Rik van Noord
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引用次数: 6

Abstract

This paper describes our participation in the shared task of Discourse Representation Structure parsing. It follows the work of Van Noord et al. (2018), who employed a neural sequence-to-sequence model to produce DRSs, also exploiting linguistic information with multiple encoders. We provide a detailed look in the performance of this model and show that (i) the benefit of the linguistic features is evident across a number of experiments which vary the amount of training data and (ii) the model can be improved by applying a number of postprocessing methods to fix ill-formed output. Our model ended up in second place in the competition, with an F-score of 84.5.
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基于IWCS的DRS解析共享任务中的神经拳击手
本文描述了我们参与篇章表示结构解析的共享任务。它遵循Van Noord等人(2018)的工作,他们采用神经序列到序列模型来生成drs,也利用多个编码器的语言信息。我们对该模型的性能进行了详细的研究,并表明:(i)语言特征的好处在许多不同训练数据量的实验中是明显的,(ii)可以通过应用一些后处理方法来修复病态输出来改进模型。我们的模型以84.5的f分在比赛中获得第二名。
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Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model Transition-based DRS Parsing Using Stack-LSTMs Neural Boxer at the IWCS Shared Task on DRS Parsing
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