Paper Info
Title
Visualizing the Relationship Between Encoded Linguistic Information and Task Performance
Abstract
Probing is popular to analyze whether linguistic information can be captured by a well-trained deep neural model, but it is hard to answer how the change of the encoded linguistic information will affect task performance. To this end, we study the dynamic relationship between the encoded linguistic information and task performance from the viewpoint of Pareto Optimality. Its key idea is to obtain a set of models which are Pareto-optimal in terms of both objectives. From this viewpoint, we propose a method to optimize the Pareto-optimal models by formalizing it as a multi-objective optimization problem. We conduct experiments on two popular NLP tasks, i.e., machine translation and language modeling, and investigate the relationship between several kinds of linguistic information and task performances. Experimental results demonstrate that the proposed method is better than a baseline method. Our empirical findings suggest that some syntactic information is helpful for NLP tasks whereas encoding more syntactic information does not necessarily lead to better performance, because the model architecture is also an important factor.
Year
DOI
Venue
2022
10.18653/v1/2022.findings-acl.35
FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022)
DocType
Volume
Citations 
Conference
Findings of the Association for Computational Linguistics: ACL 2022
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jiannan Xiang101.01
Huayang Li201.35
Defu Lian301.01
Guoping Huang432.08
Taro Watanabe500.34
Lemao Liu68718.74