Learning disentangled representations of textual data is essential for many natural language
tasks such as fair classification, style transfer and sentence generation, among others. The …

Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key
component of spoken dialog systems. In this work, we propose a new approach to learn …

When working with textual data, a natural application of disentangled representations is fair
classification where the goal is to make predictions without being biased (or influenced) by …

A new metric\texttt {BaryScore} to evaluate text generation based on deep contextualized
embeddings eg, BERT, Roberta, ELMo) is introduced. This metric is motivated by a new …

Assessing the quality of natural language generation (NLG) systems through human
annotation is very expensive. Additionally, human annotation campaigns are time …

Emotion-cause pair extraction is a recently proposed task that aims at extracting all potential
clause-level pairs of emotion and cause in text. To solve this task, researchers first proposed …

Abstract In Machine Learning, a benchmark refers to an ensemble of datasets associated
with one or multiple metrics together with a way to aggregate different systems …

Exploring the intricate dynamics between muscular and skeletal structures is pivotal for
understanding human motion. This domain presents substantial challenges, primarily …

As artificial intelligence and digitalization technologies are flourishing real-time, online
interaction–based commercial modes, exploiting customers' purchase intention implied in …

Dialogue Act (DA) classification is the task of classifying utterances with respect to the
function they serve in a dialogue. Existing approaches to DA classification model utterances …