Search Results for author:
Found 6 papers, 6 papers with code
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition
Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers.
A Graph-Based Neural Model for End-to-End Frame Semantic Parsing
The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem.
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations
Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy.
Domain-Specific NER via Retrieving Correlated Samples
Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge.
Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation
Accordingly, we manually annotate a dataset to facilitate the investigation of the newly-introduced task and build several benchmark encoder-decoder models by using VL-BART and VL-T5 as backbones.
A Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging
Self-training is one promising solution for it, which struggles to construct a set of high-quality pseudo training instances for the target domain.
