Search Results for author: Richard Evans

Found 21 papers, 6 papers with code

SemEval-2021 Task 1: Lexical Complexity Prediction

no code implementations1 Jun 2021 Matthew Shardlow, Richard Evans, Gustavo Henrique Paetzold, Marcos Zampieri

This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction.

Lexical Complexity Prediction

Predicting Lexical Complexity in English Texts

no code implementations17 Feb 2021 Matthew Shardlow, Richard Evans, Marcos Zampieri

The first step in most text simplification is to predict which words are considered complex for a given target population before carrying out lexical substitution.

Complex Word Identification Text Simplification

Evaluating the Apperception Engine

no code implementations9 Jul 2020 Richard Evans, Jose Hernandez-Orallo, Johannes Welbl, Pushmeet Kohli, Marek Sergot

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.

Inductive logic programming Unity

Classifying Referential and Non-referential It Using Gaze

1 code implementation EMNLP 2018 Victoria Yaneva, Le An Ha, Richard Evans, Ruslan Mitkov

When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones.

Eye Tracking

Making sense of sensory input

1 code implementation5 Oct 2019 Richard Evans, Jose Hernandez-Orallo, Johannes Welbl, Pushmeet Kohli, Marek Sergot

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.

Program Synthesis Unity

Sentence Simplification for Semantic Role Labelling and Information Extraction

no code implementations RANLP 2019 Richard Evans, Constantin Orasan

The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks.

Inductive general game playing

2 code implementations23 Jun 2019 Andrew Cropper, Richard Evans, Mark Law

This problem is central to inductive general game playing (IGGP).

Inductive logic programming

WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony

no code implementations SEMEVAL 2018 Omid Rohanian, Shiva Taslimipoor, Richard Evans, Ruslan Mitkov

This paper describes the systems submitted to SemEval 2018 Task 3 {``}Irony detection in English tweets{''} for both subtasks A and B.

Sentiment Analysis

Can Neural Networks Understand Logical Entailment?

no code implementations ICLR 2018 Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette

We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task.

Learning Explanatory Rules from Noisy Data

2 code implementations13 Nov 2017 Richard Evans, Edward Grefenstette

Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised.

Inductive logic programming

Combining Multiple Corpora for Readability Assessment for People with Cognitive Disabilities

no code implementations WS 2017 Victoria Yaneva, Constantin Or{\u{a}}san, Richard Evans, Omid Rohanian

Given the lack of large user-evaluated corpora in disability-related NLP research (e. g. text simplification or readability assessment for people with cognitive disabilities), the question of choosing suitable training data for NLP models is not straightforward.

Text Simplification

Deep Reinforcement Learning in Large Discrete Action Spaces

2 code implementations24 Dec 2015 Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Coppin

Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems.

Recommendation Systems

Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions

no code implementations3 Dec 2015 Peter Sunehag, Richard Evans, Gabriel Dulac-Arnold, Yori Zwols, Daniel Visentin, Ben Coppin

Further, we use deep deterministic policy gradients to learn a policy that for each position of the slate, guides attention towards the part of the action space in which the value is the highest and we only evaluate actions in this area.

Q-Learning Recommendation Systems

Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP

no code implementations21 Feb 2015 Richard Evans

Through simulated dopaminergic neurons we show how the robot is able to learn a sequence of behaviours in order to achieve a food reward.

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