Search Results for author:
Found 10 papers, 0 papers with code
Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models
Abstractive summarization models often generate factually inconsistent content particularly when the parametric knowledge of the model conflicts with the knowledge in the input document.
Is Cross-modal Information Retrieval Possible without Training?
Embeddings for a particular modality of data occupy a high-dimensional space of its own, but it can be semantically aligned to another by a simple mapping without training a deep neural net.
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision
The recent advances in representation learning inspire us to take on the challenging problem of unsupervised image classification tasks in a principled way.
Shuffle & Divide: Contrastive Learning for Long Text
We propose a self-supervised learning method for long text documents based on contrastive learning.
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization
We build and fine-tune an abstractive dialogue summarization model on a shared encoder-decoder architecture using the enhanced BERT.
Abstractive Dialogue Summarization
Abstractive Text Summarization
+4
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection
The advancement in numerous generative models has a two-fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of those images.
KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding
A Lite BERT (ALBERT) has been introduced to scale up deep bidirectional representation learning for natural languages.
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks
In zero-shot cross-lingual transfer, a supervised NLP task trained on a corpus in one language is directly applicable to another language without any additional training.
Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks
The pre-trained BERT and A Lite BERT (ALBERT) models can be fine-tuned to give state-ofthe-art results in sentence-pair regressions such as semantic textual similarity (STS) and natural language inference (NLI).
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning
This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization.
