The language ability of Large Language Models (LLMs) is often unbalanced...
Explainable natural language inference aims to provide a mechanism to pr...
Sentences that present a complex syntax act as a major stumbling block f...
The derivation of mathematical results in specialised fields using Large...
Inferring over and extracting information from Large Language Models (LL...
Deep Learning (DL) techniques now constitute the state-of-the-art for
im...
Whether Transformers can learn to apply symbolic rules and generalise to...
Rigorous evaluation of the causal effects of semantic features on langua...
Neural-based word embeddings using solely distributional information hav...
How can we interpret and retrieve medical evidence to support clinical
d...
This paper describes the results of SemEval 2023 task 7 – Multi-Evidence...
Disentangling sentence representations over continuous spaces can be a
c...
Probing strategies have been shown to detect the presence of various
lin...
The recent evolution in Natural Language Processing (NLP) methods, in
pa...
In this work, we examined Business Process (BP) production as a signal; ...
Disentangling the encodings of neural models is a fundamental aspect for...
This paper proposes a novel productivity estimation model to evaluate th...
Integer Linear Programming (ILP) provides a viable mechanism to encode
e...
Entailment trees have been proposed to simulate the human reasoning proc...
In this paper we provide a structured literature analysis focused on Dee...
Cytokine release syndrome (CRS), also known as cytokine storm, is one of...
Informal mathematical text underpins real-world quantitative reasoning a...
A fundamental research goal for Explainable AI (XAI) is to build models ...
Metamorphic testing has recently been used to check the safety of neural...
This paper contributes with a pragmatic evaluation framework for explain...
BioBERT and BioMegatron are Transformers models adapted for the biomedic...
With the methodological support of probing (or diagnostic classification...
In order for language models to aid physics research, they must first en...
In this article, we analyse how decentralised digital infrastructures ca...
In the interest of interpreting neural NLI models and their reasoning
st...
Reinforcement Learning has shown success in a number of complex virtual
...
Models designed for intelligent process automation are required to be ca...
The ability of learning disentangled representations represents a major ...
Regenerating natural language explanations for science questions is a
ch...
This paper proposes a novel statistical corpus analysis framework target...
We present a context-preserving text simplification (TS) approach that
r...
Natural language contexts display logical regularities with respect to
s...
This paper describes N-XKT (Neural encoding based on eXplanatory Knowled...
Constrained optimization solvers with Integer Linear programming (ILP) h...
An emerging line of research in Explainable NLP is the creation of datas...
This paper explores the topic of transportability, as a sub-area of
gene...
Diagrams are often used in scholarly communication. We analyse a corpus ...
Complex systems, such as Artificial Intelligence (AI) systems, are compr...
This short paper examines diagrams describing neural network systems in
...
Mobility and transport, by their nature, involve crowds and require the
...
Probing (or diagnostic classification) has become a popular strategy for...
Derivation in physics, in the form of derivation reconstruction of publi...
Accurately identifying different representations of the same real-world
...
We propose a novel approach for answering and explaining multiple-choice...
This paper presents a systematic review of benchmarks and approaches for...