Search Results for author:
Found 8 papers, 5 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.
Transfer Learning for Entity Recognition of Novel Classes
Our results empirically demonstrate when each of the published approaches tends to do well.
Entity Extraction using GAN
Named Entity Recognition (NER)
+2
WiCE: Real-World Entailment for Claims in Wikipedia
Textual entailment models are increasingly applied in settings like fact-checking, presupposition verification in question answering, or summary evaluation.
Leveraging WordNet Paths for Neural Hypernym Prediction
We formulate the problem of hypernym prediction as a sequence generation task, where the sequences are taxonomy paths in WordNet.
Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways
We pretrained our masked language models with three ingredients: an initial pretraining with music data, training on shorter sequences before training on longer ones, and masking specific tokens to target some of the BLiMP subtasks.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards
Developing documentation guidelines and easy-to-use templates for datasets and models is a challenging task, especially given the variety of backgrounds, skills, and incentives of the people involved in the building of natural language processing (NLP) tools.
X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs
Understanding when two pieces of text convey the same information is a goal touching many subproblems in NLP, including textual entailment and fact-checking.
