Search Results for author:
Found 9 papers, 5 papers with code
Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling
However, these methods are only trained at a single granularity (i. e., either token level or span level) and have some weaknesses of the corresponding granularity.
Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning
For deep ordinal classification, learning a well-structured feature space specific to ordinal classification is helpful to properly capture the ordinal nature among classes.
Learning to Classify Open Intent via Soft Labeling and Manifold Mixup
In our method, soft labeling is used to reshape the label distribution of the known intent samples, aiming at reducing model's overconfident on known intents.
Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings
While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, such vectors continue to play an important role in tasks where words need to be modelled in the absence of linguistic context.
A Symmetric Local Search Network for Emotion-Cause Pair Extraction
Each subnetwork is composed of a clause representation learner and a local pair searcher.
Enriching Word Embeddings with Domain Knowledge for Readability Assessment
In this paper, we present a method which learns the word embedding for readability assessment.
CAMREP- Concordia Action and Motion Repository
Action recognition, motion classification, gait analysis and synthesis are fundamental problems in a number of fields such as computer graphics, bio-mechanics and human computer interaction that generate a large body of research.
3DUNDERWORLD-SLS: An Open-Source Structured-Light Scanning System for Rapid Geometry Acquisition
Recently, there has been an increase in the demand of virtual 3D objects representing real-life objects.
