The standard approach for neural topic modeling uses a variational
autoe...
Tailoring outputs of large language models, such as ChatGPT, to specific...
The Natural Language for Optimization (NL4Opt) Competition was created t...
Discourse processing suffers from data sparsity, especially for dialogue...
Discourse analysis and discourse parsing have shown great impact on many...
With a growing need for robust and general discourse structures in many
...
Transition to Adulthood is an essential life stage for many families. Th...
Recent neural supervised topic segmentation models achieve distinguished...
Despite the success of recent abstractive summarizers on automatic evalu...
Conversational data is essential in psychology because it can help
resea...
With a growing number of BERTology work analyzing different components o...
RST-style discourse parsing plays a vital role in many NLP tasks, reveal...
The transformer multi-head self-attention mechanism has been thoroughly
...
Recently proposed pre-trained generation models achieve strong performan...
Transformers are the dominant architecture in NLP, but their training an...
The proliferation of text messaging for mobile health is generating a la...
Dialogue topic segmentation is critical in several dialogue modeling
pro...
Aiming for a better integration of data-driven and linguistically-inspir...
In news articles the lead bias is a common phenomenon that usually domin...
Previous work indicates that discourse information benefits summarizatio...
Discourse information, as postulated by popular discourse theories, such...
The multi-head self-attention of popular transformer models is widely us...
Our analysis of large summarization datasets indicates that redundancy i...
RST-based discourse parsing is an important NLP task with numerous downs...
Sentiment analysis, especially for long documents, plausibly requires me...
The lack of large and diverse discourse treebanks hinders the applicatio...
We consider the problem of Visual Question Answering (VQA). Given an ima...
Topic segmentation is critical in key NLP tasks and recent works favor h...
This paper evaluates the utility of Rhetorical Structure Theory (RST) tr...
Social media is a rich source where we can learn about people's reaction...
Discourse parsing could not yet take full advantage of the neural NLP
re...
In this paper, we propose a novel neural single document extractive
summ...
Probabilistic topic models such as latent Dirichlet allocation (LDA) are...
Topic segmentation and labeling is often considered a prerequisite for
h...