3 research outputs found
Improving a Strong Neural Parser with Conjunction-Specific Features
While dependency parsers reach very high overall accuracy, some dependency
relations are much harder than others. In particular, dependency parsers
perform poorly in coordination construction (i.e., correctly attaching the
"conj" relation). We extend a state-of-the-art dependency parser with
conjunction-specific features, focusing on the similarity between the conjuncts
head words. Training the extended parser yields an improvement in "conj"
attachment as well as in overall dependency parsing accuracy on the Stanford
dependency conversion of the Penn TreeBank
Controlling Linguistic Style Aspects in Neural Language Generation
Most work on neural natural language generation (NNLG) focus on controlling
the content of the generated text. We experiment with controlling several
stylistic aspects of the generated text, in addition to its content. The method
is based on conditioned RNN language model, where the desired content as well
as the stylistic parameters serve as conditioning contexts. We demonstrate the
approach on the movie reviews domain and show that it is successful in
generating coherent sentences corresponding to the required linguistic style
and content