Аннотацияланған Оқу Деректері

Corpus-based grz.mmar induction relies on us- ing many hand-parsed sentences as training examples. However, the construction of a training corpus with detailed syntactic analy- sis for every sentence is a …...

Most state-of-the-art wide-coverage parsers are trained on newspaper text and suffer a loss of accuracy in other domains, making parser adaptation a pressing issue. In this paper we demonstrate that …...

Current approaches to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sentences to logi- cal forms. Providing this supervision is …...

Supervised word sense disambiguation (WSD) systems are usually the best per- forming systems when evaluated on stan- dard benchmarks. However, these systems need annotated training data to function properly. While …...

AbstractSlot-filling models in task-driven dialog systems rely on carefully annotated training data. However, annotations by crowd workers are often inconsistent or contain errors. Simple solutions like manually checking annotations or …...

AbstractOne of the main bottlenecks in developing discourse dependency parsers is the lack of annotated training data. A potential solution is to utilize abundant unlabeled data by using unsupervised techniques, …...

AbstractCrowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the …...

AbstractMost language understanding models in task-oriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution. However, these …...

AbstractTargeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information. We assert that if models robustly encode subject-verb agreement, they should be able to …...