Search Results for author:
Found 56 papers, 18 papers with code
Graphene: Semantically-Linked Propositions in Open Information Extraction
We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification.
Graphene: A Context-Preserving Open Information Extraction System
In that way, we preserve the context of the relational tuples extracted from a source sentence, generating a novel lightweight semantic representation for Open IE that enhances the expressiveness of the extracted propositions.
Context-Preserving Text Simplification
We present a context-preserving text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences.
NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports
In this work, we present a novel resource to advance research on NLI for reasoning on CTRs.
Identifying and Explaining Discriminative Attributes
Identifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task.
Ranked #5 on
Relation Extraction
on SemEval 2018 Task 10
Does My Representation Capture X? Probe-Ably
Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models.
Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration
Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and explainable inference.
Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach
In addition to their evaluation in few-shot settings, we explore the potential of open Large Language Models (Vicuna-13B) as synthetic data generator and propose a new workflow for this purpose.
Unification-based Reconstruction of Multi-hop Explanations for Science Questions
This paper presents a novel framework for reconstructing multi-hop explanations in science Question Answering (QA).
TextGraphs 2022 Shared Task on Natural Language Premise Selection
In this summary paper, we present the results of the 1st edition of the NLPS task, providing a description of the evaluation data, and the participating systems.
A Framework for Evaluation of Machine Reading Comprehension Gold Standards
Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text.
Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making
This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support.
Multi-Operational Mathematical Derivations in Latent Space
To this end, we introduce different multi-operational representation paradigms, modelling mathematical operations as explicit geometric transformations.
Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinement
An increasing amount of research in Natural Language Inference (NLI) focuses on the application and evaluation of Large Language Models (LLMs) and their reasoning capabilities.
A Survey on Open Information Extraction
We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction.
A Sentence Simplification System for Improving Relation Extraction
In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems.
Object classification in images of Neoclassical furniture using Deep Learning
This short paper outlines research results on object classification in images of Neoclassical furniture.
Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition
Adopting a conceptual model composed of a set of semantic roles for dictionary definitions, we trained a classifier for automatically labeling definitions, preparing the data to be later converted to a graph representation.
Semantic Relation Classification: Task Formalisation and Refinement
This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora.
Categorization of Semantic Roles for Dictionary Definitions
Understanding the semantic relationships between terms is a fundamental task in natural language processing applications.
Word Tagging with Foundational Ontology Classes: Extending the WordNet-DOLCE Mapping to Verbs
Semantic annotation is fundamental to deal with large-scale lexical information, mapping the information to an enumerable set of categories over which rules and algorithms can be applied, and foundational ontology classes can be used as a formal set of categories for such tasks.
The Diagrammatic AI Language (DIAL): Version 0.1
Currently, there is no consistent model for visually or formally representing the architecture of AI systems.
On the Semantic Interpretability of Artificial Intelligence Models
Artificial Intelligence models are becoming increasingly more powerful and accurate, supporting or even replacing humans' decision making.
Understanding scholarly Natural Language Processing system diagrams through application of the Richards-Engelhardt framework
We utilise Richards-Engelhardt framework as a tool for understanding Natural Language Processing systems diagrams.
A Framework for Improving Scholarly Neural Network Diagrams
Neural networks are a prevalent and effective machine learning component, and their application is leading to significant scientific progress in many domains.
XTE: Explainable Text Entailment
Text entailment, the task of determining whether a piece of text logically follows from another piece of text, is a key component in NLP, providing input for many semantic applications such as question answering, text summarization, information extraction, and machine translation, among others.
Case-Based Abductive Natural Language Inference
Most of the contemporary approaches for multi-hop Natural Language Inference (NLI) construct explanations considering each test case in isolation.
A Survey on Explainability in Machine Reading Comprehension
This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC).
ExplanationLP: Abductive Reasoning for Explainable Science Question Answering
We propose a novel approach for answering and explaining multiple-choice science questions by reasoning on grounding and abstract inference chains.
Similarity-Based Equational Inference in Physics
Automating the derivation of published results is a challenge, in part due to the informal use of mathematics by physicists, compared to that of mathematicians.
Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards
An emerging line of research in Explainable NLP is the creation of datasets enriched with human-annotated explanations and rationales, used to build and evaluate models with step-wise inference and explanation generation capabilities.
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference
This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differentiable convex optimization.
Supporting Context Monotonicity Abstractions in Neural NLI Models
Natural language contexts display logical regularities with respect to substitutions of related concepts: these are captured in a functional order-theoretic property called monotonicity.
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders
The ability of learning disentangled representations represents a major step for interpretable NLP systems as it allows latent linguistic features to be controlled.
Diagrammatic summaries for neural architectures
This paper advocates for diagrammatic summary publications for machine learning system architecture papers.
Transformers and the representation of biomedical background knowledge
Specialised transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora.
Scientific Explanation and Natural Language: A Unified Epistemological-Linguistic Perspective for Explainable AI
A fundamental research goal for Explainable AI (XAI) is to build models that are capable of reasoning through the generation of natural language explanations.
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e. g. pathways or Protein-Protein-Interaction networks) and interpretability.
Active entailment encoding for explanation tree construction using parsimonious generation of hard negatives
Entailment trees have been proposed to simulate the human reasoning process of explanation generation in the context of open--domain textual question answering.
Going Beyond Approximation: Encoding Constraints for Explainable Multi-hop Inference via Differentiable Combinatorial Solvers
Integer Linear Programming (ILP) provides a viable mechanism to encode explicit and controllable assumptions about explainable multi-hop inference with natural language.
Text Classification and Prediction in the Legal Domain
We present a case study on the application of text classification and legal judgment prediction for flight compensation.
Estimating productivity gains in digital automation
This paper proposes a novel productivity estimation model to evaluate the effects of adopting Artificial Intelligence (AI) components in a production chain.
Shallow Discourse Parsing for Open Information Extraction and Text Simplification
We present a discourse-aware text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences.
Quasi-symbolic explanatory NLI via disentanglement: A geometrical examination
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control, and understanding downstream task performance in Natural Language Processing.
Montague semantics and modifier consistency measurement in neural language models
At the same time, the need for interpretability has elicited questions on their intrinsic properties and capabilities.
Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks
Disentangled latent spaces usually have better semantic separability and geometrical properties, which leads to better interpretability and more controllable data generation.
SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data
This paper describes the results of SemEval 2023 task 7 -- Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT) -- consisting of 2 tasks, a Natural Language Inference (NLI) task, and an evidence selection task on clinical trial data.
Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions
Natural language definitions possess a recursive, self-explanatory semantic structure that can support representation learning methods able to preserve explicit conceptual relations and constraints in the latent space.
Discourse-Aware Text Simplification: From Complex Sentences to Linked Propositions
In that way, we generate a semantic hierarchy of minimal propositions that leads to a novel representation of complex assertions that puts a semantic layer on top of the simplified sentences.
Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders
The injection of syntactic information in Variational AutoEncoders (VAEs) has been shown to result in an overall improvement of performances and generalisation.
LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces
Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-level latent spaces.
Inference to the Best Explanation in Large Language Models
While Large Language Models (LLMs) have found success in real-world applications, their underlying explanatory process is still poorly understood.
A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference
Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI).
Estimating the Causal Effects of Natural Logic Features in Transformer-Based NLI Models
Rigorous evaluation of the causal effects of semantic features on language model predictions can be hard to achieve for natural language reasoning problems.
SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials
Addressing this, we present SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for ClinicalTrials.
