Structured semantic sentence representations such as Abstract Meaning Representations (AMRs) are potentially useful in a variety of natural language processing tasks. However, the quality of automatic parses can vary greatly and jeopardizes their usefulness. Therefore, we require systems that can accurately rate AMR quality in the absence of costly gold data . To achieve this , we transfer the AMR graph to the domain of images. This allows us to create a simple convolutional neural network (CNN) that imitates a human rater. In our experiments , we show that the method can rate the quality of AMR graphs more accurately than a strong baseline, with respect to several dimensions of interest. Furthermore , the method proves to be more efficient as it reduces the incurred energy consumption.
Structured semantic sentence representations such as Abstract Meaning Representations (AMRs) are potentially useful in various NLP tasks. However, the quality of automatic parses can vary greatly and jeopardizes their usefulness. This can be mitigated by models that can accurately rate AMR quality in the absence of costly gold data , allowing us to inform downstream systems about an incorporated parse's trustworthiness or select among different candidate parses. In this work, we propose to transfer the AMR graph to the domain of images. This allows us to create a simple convolutional neural network (CNN) that imitates a human judge tasked with rating graph quality. Our experiments show that the method can rate quality more accurately than strong baselines, in several quality dimensions. Moreover , the method proves to be efficient and reduces the incurred energy consumption.
R
a variety of natural language processing
various NLP
R
Therefore, we require systems
This can be mitigated by models
R
. To achieve this , we
, allowing us to inform downstream systems about an incorporated parse's trustworthiness or select among different candidate parses. In this work, we propose to
R
rater. In our experiments , we
judge tasked with rating graph quality. Our experiments
R
the quality of AMR graphs
quality
R
a strong baseline, with respect to several dimensions of interest. Furthermore
strong baselines, in several quality dimensions. Moreover
R
more efficient as it
efficient and