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Found 18 papers, 10 papers with code
A Proposal: Interactively Learning to Summarise Timelines by Reinforcement Learning
Timeline Summarisation (TLS) aims to generate a concise, time-ordered list of events described in sources such as news articles.
Towards Abstractive Timeline Summarisation using Preference-based Reinforcement Learning
This paper proposes a preference-based reinforcement learning (PBRL) method for adapting pretrained abstractive summarisers to TLS, which can overcome the drawbacks of extractive timeline summaries.
Efficient Methods for Natural Language Processing: A Survey
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows.
Assisting Decision Making in Scholarly Peer Review: A Preference Learning Perspective
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.
SemEval-2021 Task 12: Learning with Disagreements
Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision.
Aggregating and Learning from Multiple Annotators
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
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
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
We compare different models for low resource multi-task sequence tagging that leverage dependencies between label sequences for different tasks.
Improving Factual Consistency Between a Response and Persona Facts
Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker's persona.
Scalable Bayesian Preference Learning for Crowds
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.
Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
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.
Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning
The inability to quantify key aspects of creative language is a frequent obstacle to natural language understanding.
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
Such applications depend on classifying the situation across a region of interest, which can be depicted as a spatial "heatmap".
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
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.
A Bayesian Approach for Sequence Tagging with Crowds
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.
Finding Convincing Arguments Using Scalable Bayesian Preference Learning
We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard rat- ings or rankings.
Space Warps: I. Crowd-sourcing the Discovery of Gravitational Lenses
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
