In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

How to transfer the semantic information in a sentence to a computable numerical
embedding form is a fundamental problem in natural language processing. An informative …

A robust evaluation metric has a profound impact on the development of text generation
systems. A desirable metric compares system output against references based on their …

We propose procedures for evaluating and strengthening contextual embedding alignment
and show that they are useful in analyzing and improving multilingual BERT. In particular …

Sentence embedding is an important research topic in natural language processing (NLP)
since it can transfer knowledge to downstream tasks. Meanwhile, a contextualized word …

As the number of AI systems keeps growing, it is fundamental to implement and develop
efficient control mechanisms to ensure the safe and proper functioning of machine learning …

Abstract Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness
and security requirements driven by an increased number of AI-based systems. Existing …

Despite the fast developmental pace of new sentence embedding methods, it is still
challenging to find comprehensive evaluations of these different techniques. In the past …

Reinforcement Learning (RL) based document summarisation systems yield state-of-the-art
performance in terms of ROUGE scores, because they directly use ROUGE as the rewards …

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 …