Neural network models have achieved state-of-the-art performances in a wide range of
natural language processing (NLP) tasks. However, a long-standing criticism against neural …

Large language models (LLMs) can learn to perform a wide range of natural language tasks
from just a handful of in-context examples. However, for generating strings from highly …

Despite a multitude of empirical studies, little consensus exists on whether neural networks
are able to generalise compositionally, a controversy that, in part, stems from a lack of …

Dependency trees have been intensively used with graph neural networks for aspect-based
sentiment classification. Though being effective, such methods rely on external dependency …

Pre-training Transformer from large-scale raw texts and fine-tuning on the desired task have
achieved state-of-the-art results on diverse NLP tasks. However, it is unclear what the …

We study a formalization of the grammar induction problem that models sentences as being
generated by a compound probabilistic context-free grammar. In contrast to traditional …

Recent advances of powerful Language Models have allowed Natural Language
Generation (NLG) to emerge as an important technology that can not only perform traditional …

Drug design is a crucial step in the drug discovery cycle. Recently, various deep learning-
based methods design drugs by generating novel molecules from scratch, avoiding …

With the recent success and popularity of pre-trained language models (LMs) in natural
language processing, there has been a rise in efforts to understand their inner workings. In …

Deep transformer models have pushed performance on NLP tasks to new limits, suggesting
sophisticated treatment of complex linguistic inputs, such as phrases. However, we have …