Sentence Representation Learning (SRL) is a fundamental task in Natural
...
Current methods for Knowledge-Based Question Answering (KBQA) usually re...
In human conversations, individuals can indicate relevant regions within...
Data augmentation is widely used in text classification, especially in t...
Few-shot text classification has recently been promoted by the meta-lear...
Opinion target extraction (OTE) or aspect extraction (AE) is a fundament...
Few-shot relation classification (RC) is one of the critical problems in...
Contrastive learning has achieved remarkable success in representation
l...
Recent researches have suggested that the predictive accuracy of neural
...
Recent research has suggested that when training neural networks, flat l...
The dependencies between system and user utterances in the same turn and...
Dialogue state tracking (DST) is an important part of a spoken dialogue
...
SkipGram word embedding models with negative sampling, or SGN in short, ...
Named entity recognition (NER) and Relation extraction (RE) are two
fund...
We consider the problem of learning a neural network classifier. Under t...
Distant supervision for relation extraction enables one to effectively
a...
In this work, we explain the working mechanism of MixUp in terms of
adve...
Mixup, a recent proposed data augmentation method through linearly
inter...
In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to st...
MixUp, a data augmentation approach through mixing random samples, has b...
We establish an equivalence between information bottleneck (IB) learning...