Search Results for author:
Found 9 papers, 6 papers with code
End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?
In this work, we design an end-to-end model for poetry generation based on conditioned recurrent neural network (RNN) language models whose goal is to learn stylistic features (poem length, sentiment, alliteration, and rhyming) from examples alone.
NLLG Quarterly arXiv Report 09/23: What are the most influential current AI Papers?
Artificial Intelligence (AI) has witnessed rapid growth, especially in the subfields Natural Language Processing (NLP), Machine Learning (ML) and Computer Vision (CV).
AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ
To address this, we propose the use of TikZ, a well-known abstract graphics language that can be compiled to vector graphics, as an intermediate representation of scientific figures.
NLLG Quarterly arXiv Report 06/23: What are the most influential current AI Papers?
In particular, we compile a list of the 40 most popular papers based on normalized citation counts from the first half of 2023.
Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?
We demonstrate the impact of our approach on an Argument Mining downstream task, evaluated on different corpora, showing that language models can be trained to automatically fill in discourse markers across different corpora, improving the performance of a downstream model in some, but not all, cases.
ChatGPT: A Meta-Analysis after 2.5 Months
ChatGPT, a chatbot developed by OpenAI, has gained widespread popularity and media attention since its release in November 2022.
ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language Models
State-of-the-art poetry generation systems are often complex.
Reproducibility Issues for BERT-based Evaluation Metrics
We find that reproduction of claims and results often fails because of (i) heavy undocumented preprocessing involved in the metrics, (ii) missing code and (iii) reporting weaker results for the baseline metrics.
USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation
We show that our fully unsupervised metrics are effective, i. e., they beat supervised competitors on 4 out of our 5 evaluation datasets.
