Leveraging External Knowledge for Out-Of-Vocabulary Entity Labeling
Dealing with previously unseen slots is a challenging problem in a real-world multi-domain dialogue state tracking task. Other approaches rely on predefined mappings to generate candidate slot keys, as well as their associated values. This, however, may fail when the key, the value, or both, are not seen during training. To address this problem we introduce a neural network that leverages external knowledge bases (KBs) to better classify out-of-vocabulary slot keys and values. This network projects the slot into an attribute space derived from the KB, and, by leveraging similarities in this space, we propose candidate slot keys and values to the dialogue state tracker. We provide extensive experiments that demonstrate that our stratagem can improve upon a previous approach, which relies on predefined candidate mappings. In particular, we evaluate this approach by training a state-of-the-art model with candidates generated from our network, and obtained relative increases of 57.7% and 82.7% in F1 score and accuracy, respectively, for the aforementioned model, when compared to the current candidate generation strategy.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeDialogue State TrackingMulti-domain Dialogue State TrackingSimilar Papers 제목 키워드 기반
Mining Entity Synonyms with Efficient Neural Set Generation
Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term,…
Handling Rare Entities for Neural Sequence Labeling
One great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases. Most of test set entities appear only few times and are even unseen in training corpus, yielding large numbe…
VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition
Prevalent solution for BioNER involves using representation learning techniques coupled with sequence labeling. However, such methods are inherently task-specific, demonstrate poor generalizability, and often require ded…
Language ModelingLanguage ModellingLarge Language Modelnamed-entity-recognition+2Generating Fine-Grained Open Vocabulary Entity Type Descriptions
While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge…
Knowledge GraphsVocal Bursts Type PredictionLeveraging User Engagement Signals For Entity Labeling in a Virtual Assistant
Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands. However, components in these systems generally rely on supervised m…
Data AugmentationMulti-Task Learning