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Found 23 papers, 7 papers with code
Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals
We illustrate Dramatron's usefulness as an interactive co-creative system with a user study of 15 theatre and film industry professionals.
Confusion Matrices and Accuracy Statistics for Binary Classifiers Using Unlabeled Data: The Diagnostic Test Approach
That problem is the same as estimating confusion matrices for classifiers on unlabeled data.
Highly accurate protein structure prediction with AlphaFold
Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.
SemEval-2021 Task 1: Lexical Complexity Prediction
This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction.
Inductive logic programming at 30
Inductive logic programming (ILP) is a form of logic-based machine learning.
Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset
We develop a protocol for the annotation of lexical complexity and use this to annotate a new dataset, CompLex 2. 0.
Complex Word Identification
Lexical Complexity Prediction
+1
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.
Classifying Referential and Non-referential It Using Gaze
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.
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.
Sentence Simplification for Semantic Role Labelling and Information Extraction
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.
WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony
This paper describes the systems submitted to SemEval 2018 Task 3 {``}Irony detection in English tweets{''} for both subtasks A and B.
Can Neural Networks Understand Logical Entailment?
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
Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised.
Combining Multiple Corpora for Readability Assessment for People with Cognitive Disabilities
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.
Deep Reinforcement Learning in Large Discrete Action Spaces
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.
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
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.
Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP
Through simulated dopaminergic neurons we show how the robot is able to learn a sequence of behaviours in order to achieve a food reward.
