4,022 research outputs found
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
Many efforts of research are devoted to semantic role labeling (SRL) which is
crucial for natural language understanding. Supervised approaches have achieved
impressing performances when large-scale corpora are available for
resource-rich languages such as English. While for the low-resource languages
with no annotated SRL dataset, it is still challenging to obtain competitive
performances. Cross-lingual SRL is one promising way to address the problem,
which has achieved great advances with the help of model transferring and
annotation projection. In this paper, we propose a novel alternative based on
corpus translation, constructing high-quality training datasets for the target
languages from the source gold-standard SRL annotations. Experimental results
on Universal Proposition Bank show that the translation-based method is highly
effective, and the automatic pseudo datasets can improve the target-language
SRL performances significantly.Comment: Accepted at ACL 202
A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training
The successful application of large pre-trained models such as BERT in
natural language processing has attracted more attention from researchers.
Since the BERT typically acts as an end-to-end black box, classification
systems based on it usually have difficulty in interpretation and low
robustness. This paper proposes a visual interpretation-based self-improving
classification model with a combination of virtual adversarial training (VAT)
and BERT models to address the above problems. Specifically, a fine-tuned BERT
model is used as a classifier to classify the sentiment of the text. Then, the
predicted sentiment classification labels are used as part of the input of
another BERT for spam classification via a semi-supervised training manner
using VAT. Additionally, visualization techniques, including visualizing the
importance of words and normalizing the attention head matrix, are employed to
analyze the relevance of each component to classification accuracy. Moreover,
brand-new features will be found in the visual analysis, and classification
performance will be improved. Experimental results on Twitter's tweet dataset
demonstrate the effectiveness of the proposed model on the classification task.
Furthermore, the ablation study results illustrate the effect of different
components of the proposed model on the classification results
- …