Search Results for author:
Found 17 papers, 12 papers with code
Distilling Relation Embeddings from Pretrained Language Models
Among others, this makes it possible to distill high-quality word vectors from pre-trained language models.
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research
Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks.
RelBERT: Embedding Relations with Language Models
In particular, we show that masked language models such as RoBERTa can be straightforwardly fine-tuned for this purpose, using only a small amount of training data.
A Practical Toolkit for Multilingual Question and Answer Generation
Generating questions along with associated answers from a text has applications in several domains, such as creating reading comprehension tests for students, or improving document search by providing auxiliary questions and answers based on the query.
An Empirical Comparison of LM-based Question and Answer Generation Methods
This task has a variety of applications, such as data augmentation for question answering (QA) models, information retrieval and education.
An Efficient Multilingual Language Model Compression through Vocabulary Trimming
Multilingual language model (LM) have become a powerful tool in NLP especially for non-English languages.
A RelEntLess Benchmark for Modelling Graded Relations between Named Entities
Relations such as "is influenced by", "is known for" or "is a competitor of" are inherently graded: we can rank entity pairs based on how well they satisfy these relations, but it is hard to draw a line between those pairs that satisfy them and those that do not.
Generative Language Models for Paragraph-Level Question Generation
It includes general-purpose datasets such as SQuAD for English, datasets from ten domains and two styles, as well as datasets in eight different languages.
Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts
Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER).
Twitter Topic Classification
Social media platforms host discussions about a wide variety of topics that arise everyday.
T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition
In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning.
Ranked #4 on
Named Entity Recognition (NER)
on WNUT 2017
TweetNLP: Cutting-Edge Natural Language Processing for Social Media
In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media.
Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification
Data augmentation techniques are widely used for enhancing the performance of machine learning models by tackling class imbalance issues and data sparsity.
Distilling Relation Embeddings from Pre-trained Language Models
Among others, this makes it possible to distill high-quality word vectors from pre-trained language models.
BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?
Analogies play a central role in human commonsense reasoning.
Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction
Term weighting schemes are widely used in Natural Language Processing and Information Retrieval.
Latent Space Cartography: Generalised Metric-Inspired Measures and Measure-Based Transformations for Generative Models
Deep generative models are universal tools for learning data distributions on high dimensional data spaces via a mapping to lower dimensional latent spaces.
