Search Results for author: Edwin Simpson

Found 16 papers, 8 papers with code

A Proposal: Interactively Learning to Summarise Timelines by Reinforcement Learning

no code implementations ACL (InterNLP) 2021 Yuxuan Ye, Edwin Simpson

Timeline Summarisation (TLS) aims to generate a concise, time-ordered list of events described in sources such as news articles.


Assisting Decision Making in Scholarly Peer Review: A Preference Learning Perspective

no code implementations2 Sep 2021 Nils Dycke, Edwin Simpson, Ilia Kuznetsov, Iryna Gurevych

Peer review is the primary means of quality control in academia; as an outcome of a peer review process, program and area chairs make acceptance decisions for each paper based on the review reports and scores they received.

Decision Making Fairness

SemEval-2021 Task 12: Learning with Disagreements

no code implementations SEMEVAL 2021 Alexandra Uma, Tommaso Fornaciari, Anca Dumitrache, Tristan Miller, Jon Chamberlain, Barbara Plank, Edwin Simpson, Massimo Poesio

Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision.

Natural Language Processing

Aggregating and Learning from Multiple Annotators

no code implementations EACL 2021 Silviu Paun, Edwin Simpson

There is also a growing body of recent work arguing that following the convention and training with adjudicated labels ignores any uncertainty the labellers had in their classifications, which results in models with poorer generalisation capabilities.

Ranking Creative Language Characteristics in Small Data Scenarios

no code implementations23 Oct 2020 Julia Siekiera, Marius Köppel, Edwin Simpson, Kevin Stowe, Iryna Gurevych, Stefan Kramer

We therefore adapt the DirectRanker to provide a new deep model for ranking creative language with small data.

Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning

1 code implementation3 Aug 2020 Tristan Miller, Erik-Lân Do Dinh, Edwin Simpson, Iryna Gurevych

Most humour processing systems to date make at best discrete, coarse-grained distinctions between the comical and the conventional, yet such notions are better conceptualized as a broad spectrum.

Low Resource Multi-Task Sequence Tagging -- Revisiting Dynamic Conditional Random Fields

no code implementations1 May 2020 Jonas Pfeiffer, Edwin Simpson, Iryna Gurevych

We compare different models for low resource multi-task sequence tagging that leverage dependencies between label sequences for different tasks.

Multi-Task Learning

Improving Factual Consistency Between a Response and Persona Facts

no code implementations EACL 2021 Mohsen Mesgar, Edwin Simpson, Iryna Gurevych

Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker's persona.

reinforcement-learning Response Generation

Scalable Bayesian Preference Learning for Crowds

1 code implementation4 Dec 2019 Edwin Simpson, Iryna Gurevych

As previous solutions based on Gaussian processes do not scale to large numbers of users, items or pairwise labels, we propose a stochastic variational inference approach that limits computational and memory costs.

Gaussian Processes Natural Language Processing +1

Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation

1 code implementation22 Nov 2019 Edwin Simpson, Yang Gao, Iryna Gurevych

For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs.

Bayesian Optimisation Community Question Answering +1

Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources

1 code implementation5 Apr 2019 Edwin Simpson, Steven Reece, Stephen J. Roberts

Such applications depend on classifying the situation across a region of interest, which can be depicted as a spatial "heatmap".

Classification Disaster Response +1

Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems

no code implementations NAACL 2019 Steffen Eger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych

Visual modifications to text are often used to obfuscate offensive comments in social media (e. g., "! d10t") or as a writing style ("1337" in "leet speak"), among other scenarios.

Adversarial Attack

A Bayesian Approach for Sequence Tagging with Crowds

1 code implementation IJCNLP 2019 Edwin Simpson, Iryna Gurevych

Current methods for sequence tagging, a core task in NLP, are data hungry, which motivates the use of crowdsourcing as a cheap way to obtain labelled data.

Active Learning Argument Mining +2

Finding Convincing Arguments Using Scalable Bayesian Preference Learning

1 code implementation TACL 2018 Edwin Simpson, Iryna Gurevych

We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard rat- ings or rankings.

Active Learning Variational Inference +1

Space Warps: I. Crowd-sourcing the Discovery of Gravitational Lenses

1 code implementation21 Apr 2015 Philip J. Marshall, Aprajita Verma, Anupreeta More, Christopher P. Davis, Surhud More, Amit Kapadia, Michael Parrish, Chris Snyder, Julianne Wilcox, Elisabeth Baeten, Christine Macmillan, Claude Cornen, Michael Baumer, Edwin Simpson, Chris J. Lintott, David Miller, Edward Paget, Robert Simpson, Arfon M. Smith, Rafael Küng, Prasenjit Saha, Thomas E. Collett, Matthias Tecza

We describe Space Warps, a novel gravitational lens discovery service that yields samples of high purity and completeness through crowd-sourced visual inspection.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies

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