Search Results for author:
Found 7 papers, 3 papers with code
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods.
QuestEval: Summarization Asks for Fact-based Evaluation
Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments.
ColdGANs: Taming Language GANs with Cautious Sampling Strategies
Training regimes based on Maximum Likelihood Estimation (MLE) suffer from known limitations, often leading to poorly generated text sequences.
MLSUM: The Multilingual Summarization Corpus
We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
What BERT Sees: Cross-Modal Transfer for Visual Question Generation
Pre-trained language models have recently contributed to significant advances in NLP tasks.
Discriminative Adversarial Search for Abstractive Summarization
We introduce a novel approach for sequence decoding, Discriminative Adversarial Search (DAS), which has the desirable properties of alleviating the effects of exposure bias without requiring external metrics.
