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09/16/2023
Temporal Smoothness Regularisers for Neural Link Predictors
Most algorithms for representation learning and link prediction on relat...
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08/12/2023
Approximate Answering of Graph Queries
Knowledge graphs (KGs) are inherently incomplete because of incomplete w...
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07/12/2023
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models
The computation necessary for training Transformer-based language models...
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07/06/2023
Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain
Adapting pretrained language models to novel domains, such as clinical a...
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05/31/2023
Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks
Knowledge graphs are powerful tools for representing and organising comp...
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05/22/2023
SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations
Explaining the decisions of neural models is crucial for ensuring their ...
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05/22/2023
Logical Reasoning for Natural Language Inference Using Generated Facts as Atoms
State-of-the-art neural models can now reach human performance levels ac...
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01/29/2023
Adapting Neural Link Predictors for Complex Query Answering
Answering complex queries on incomplete knowledge graphs is a challengin...
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11/17/2022
Machine Learning-Assisted Recurrence Prediction for Early-Stage Non-Small-Cell Lung Cancer Patients
Background: Stratifying cancer patients according to risk of relapse can...
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10/30/2022
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks
Access to external knowledge is essential for many natural language proc...
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10/27/2022
Learning Discrete Directed Acyclic Graphs via Backpropagation
Recently continuous relaxations have been proposed in order to learn Dir...
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09/11/2022
Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models
The integration of discrete algorithmic components in deep learning arch...
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07/20/2022
ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective
Factorisation-based Models (FMs), such as DistMult, have enjoyed endurin...
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05/23/2022
Logical Reasoning with Span Predictions: Span-level Logical Atoms for Interpretable and Robust NLI Models
Current Natural Language Inference (NLI) models achieve impressive resul...
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04/12/2022
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking
In a task-oriented dialogue system, Dialogue State Tracking (DST) keeps ...
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04/10/2022
MedDistant19: A Challenging Benchmark for Distantly Supervised Biomedical Relation Extraction
Relation Extraction in the biomedical domain is challenging due to the l...
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03/20/2022
Differentiable Reasoning over Long Stories – Assessing Systematic Generalisation in Neural Models
Contemporary neural networks have achieved a series of developments and ...
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10/25/2021
A Probabilistic Framework for Knowledge Graph Data Augmentation
We present NNMFAug, a probabilistic framework to perform data augmentati...
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10/06/2021
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
Learning good representations on multi-relational graphs is essential to...
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07/05/2021
Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints
Adaptive Computation (AC) has been shown to be effective in improving th...
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06/03/2021
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
Integrating discrete probability distributions and combinatorial optimiz...
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02/13/2021
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
Open-domain Question Answering models which directly leverage question-a...
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02/08/2021
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning
Although reinforcement learning has been successfully applied in many do...
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01/24/2021
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models
This paper proposes two intuitive metrics, skew and stereotype, that qua...
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01/01/2021
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020. The competition...
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11/10/2020
Don't Read Too Much into It: Adaptive Computation for Open-Domain Question Answering
Most approaches to Open-Domain Question Answering consist of a light-wei...
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11/06/2020
Complex Query Answering with Neural Link Predictors
Neural link predictors are immensely useful for identifying missing edge...
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07/17/2020
WordCraft: An Environment for Benchmarking Commonsense Agents
The ability to quickly solve a wide range of real-world tasks requires a...
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07/13/2020
Learning Reasoning Strategies in End-to-End Differentiable Proving
Attempts to render deep learning models interpretable, data-efficient, a...
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04/30/2020
Knowledge Graph Embeddings and Explainable AI
Knowledge graph embeddings are now a widely adopted approach to knowledg...
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04/16/2020
There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
Natural Language Inference (NLI) datasets contain annotation artefacts r...
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02/15/2020
Undersensitivity in Neural Reading Comprehension
Current reading comprehension models generalise well to in-distribution ...
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12/17/2019
Differentiable Reasoning on Large Knowledge Bases and Natural Language
Reasoning with knowledge expressed in natural language and Knowledge Bas...
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06/14/2019
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language
Rule-based models are attractive for various tasks because they inherent...
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06/12/2019
Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings
Recent advances in Neural Variational Inference allowed for a renaissanc...
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12/16/2018
Embedding Cardinality Constraints in Neural Link Predictors
Neural link predictors learn distributed representations of entities and...
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08/26/2018
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge
Adversarial examples are inputs to machine learning models designed to c...
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07/21/2018
Towards Neural Theorem Proving at Scale
Neural models combining representation learning and reasoning in an end-...
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06/20/2018
Jack the Reader - A Machine Reading Framework
Many Machine Reading and Natural Language Understanding tasks require re...
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05/17/2018
Extrapolation in NLP
We argue that extrapolation to examples outside the training space will ...
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07/24/2017