6 papers
Controlling Linguistic Style Aspects in Neural Language Generation
Jessica Ficler, Yoav Goldberg
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 gener…
Improving a Strong Neural Parser with Conjunction-Specific Features
Jessica Ficler, Yoav Goldberg
While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others. In particular, dependency parsers perform poorly in coordination c…
A Neural Network for Coordination Boundary Prediction
Jessica Ficler, Yoav Goldberg
We propose a neural-network based model for coordination boundary prediction. The network is designed to incorporate two signals: the similarity between conjuncts and the observati…
Coordination Annotation Extension in the Penn Tree Bank
Jessica Ficler, Yoav Goldberg
Coordination is an important and common syntactic construction which is not handled well by state of the art parsers. Coordinations in the Penn Treebank are missing internal struct…
Improved Parsing for Argument-Clusters Coordination
Jessica Ficler, Yoav Goldberg
Syntactic parsers perform poorly in prediction of Argument-Cluster Coordination (ACC). We change the PTB representation of ACC to be more suitable for learning by a statistical PCF…
Getting More Out Of Syntax with PropS
Gabriel Stanovsky, Jessica Ficler, Ido Dagan +1
Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed express…