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Found 18 papers, 3 papers with code
MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms
This study investigates the computational processing of euphemisms, a universal linguistic phenomenon, across multiple languages.
Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks
In this paper, to improve the communication efficiency of federated learning in complex networks, we study the communication efficiency optimization of federated learning for computing and network convergence of 6G networks, methods that gives decisions on its training process for different network conditions and arithmetic power of participating devices in federated learning.
FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms
Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context.
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification
Leveraging transfer learning, we compare the performance of cross-domain adaptation from Twitter domain, and cross-lingual adaptation from English language.
A Report on the Euphemisms Detection Shared Task
This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022.
Relative growth rate optimization under behavioral criterion
This paper studies a continuous-time optimal portfolio selection problem in the complete market for a behavioral investor whose preference is of the prospect type with probability distortion.
FedMT: Federated Learning with Mixed-type Labels
In federated learning (FL), classifiers (e. g., deep networks) are trained on datasets from multiple data centers without exchanging data across them, which improves the sample efficiency.
Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms
This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs.
CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms
Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not.
Linguistic Fingerprints of Internet Censorship: the Case of SinaWeibo
The crowdsourcing results suggest that while humans tend to see censored blogposts as more controversial and more likely to trigger action in real life than the uncensored counterparts, they in general cannot make a better guess than our model when it comes to `reading the mind' of the censors in deciding whether a blogpost should be censored.
Learning Action Models from Disordered and Noisy Plan Traces
Our approach takes as input a set of plan traces with disordered actions and noise and outputs action models that can best explain the plan traces.
Neural Network Prediction of Censorable Language
According to Freedom House{'}s annual Freedom on the Net report, more than half the world{'}s Internet users now live in a place where the Internet is censored or restricted.
Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation
Human-object interactions (HOI) recognition and pose estimation are two closely related tasks.
Linguistic Characteristics of Censorable Language on SinaWeibo
This paper investigates censorship from a linguistic perspective.
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions
Our starting point is that words in a given text segment, such as a paragraph, that are highranking representatives of a common topic of discussion are less likely to be a part of an idiomatic expression.
Experiments in Idiom Recognition
Some expressions can be ambiguous between idiomatic and literal interpretations depending on the context they occur in, e. g., {`}sales hit the roof{'} vs. {`}hit the roof of the car{'}.
