When Do Discourse Markers Affect Computational Sentence Understanding?
Ruiqi Li Liesbeth Allein Damien Sileo Marie-Francine Moens
The capabilities and use cases of automatic natural language processing (NLP)
have grown significantly over the last few years. While much work has been
devoted to understanding how humans deal with discourse connectives, this
phenomenon is understudied in computational systems. Therefore, it is important
to put NLP models under the microscope and examine whether they can adequately
comprehend, process, and reason within the complexity of natural language. In
this chapter, we introduce the main mechanisms behind automatic sentence
processing systems step by step and then focus on evaluating discourse
connective processing. We assess nine popular systems in their ability to
understand English discourse connectives and analyze how context and language
understanding tasks affect their connective comprehension. The results show
that NLP systems do not process all discourse connectives equally well and that
the computational processing complexity of different connective kinds is not
always consistently in line with the presumed complexity order found in human
processing. In addition, while humans are more inclined to be influenced during
the reading procedure but not necessarily in the final comprehension
performance, discourse connectives have a significant impact on the final
accuracy of NLP systems. The richer knowledge of connectives a system learns,
the more negative effect inappropriate connectives have on it. This suggests
that the correct explicitation of discourse connectives is important for
computational natural language processing.