Search Results for author:
Found 24 papers, 11 papers with code
A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion
Embedding-based Knowledge Base Completion models have so far mostly combined distributed representations of individual entities or relations to compute truth scores of missing links.
Neural Random Forests
Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights.
Complex Embeddings for Simple Link Prediction
In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases.
Ranked #4 on
Link Prediction
on FB122
Frustratingly Short Attention Spans in Neural Language Modeling
This vector is used both for predicting the next token as well as for the key and value of a differentiable memory of a token history.
Knowledge Graph Completion via Complex Tensor Factorization
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.
Ranked #2 on
Knowledge Graphs
on FB15k
Crowdsourcing Multiple Choice Science Questions
With this method we have assembled SciQ, a dataset of 13. 7K multiple choice science exam questions (Dataset available at http://allenai. org/data. html).
Constructing Datasets for Multi-hop Reading Comprehension Across Documents
We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods.
Ranked #9 on
Question Answering
on WikiHop
Jack the Reader - A Machine Reading Framework
For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions.
Jack the Reader -- A Machine Reading Framework
For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions.
UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)
In this paper we describe our 2nd place FEVER shared-task system that achieved a FEVER score of 62. 52{\%} on the provisional test set (without additional human evaluation), and 65. 41{\%} on the development set.
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
Neural networks are part of many contemporary NLP systems, yet their empirical successes come at the price of vulnerability to adversarial attacks.
Scalable Neural Learning for Verifiable Consistency with Temporal Specifications
Formal verification of machine learning models has attracted attention recently, and significant progress has been made on proving simple properties like robustness to small perturbations of the input features.
Making sense of sensory input
This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence.
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
This paper aims to quantify and reduce a particular type of bias exhibited by language models: bias in the sentiment of generated text.
Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
We find that training on adversarially collected samples leads to strong generalisation to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop.
Ranked #1 on
Reading Comprehension
on AdversarialQA
(using extra training data)
Undersensitivity in Neural Reading Comprehension
Current reading comprehension models generalise well to in-distribution test sets, yet perform poorly on adversarially selected inputs.
Towards Verified Robustness under Text Deletion Interventions
Neural networks are widely used in Natural Language Processing, yet despite their empirical successes, their behaviour is brittle: they are both over-sensitive to small input changes, and under-sensitive to deletions of large fractions of input text.
Evaluating the Apperception Engine
This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence.
Challenges in Detoxifying Language Models
Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Ranked #1 on
Language Modelling
on StackExchange
Competition-Level Code Generation with AlphaCode
Programming is a powerful and ubiquitous problem-solving tool.
Ranked #1 on
Code Generation
on CodeContests
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models
Large language models produce human-like text that drive a growing number of applications.
Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
Radiology reports are an instrumental part of modern medicine, informing key clinical decisions such as diagnosis and treatment.
