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Found 16 papers, 5 papers with code
Guiding Principles for Participatory Design-inspired Natural Language Processing
We introduce 9 guiding principles to integrate Participatory Design (PD) methods in the development of Natural Language Processing (NLP) systems.
Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.
Cross-target generalization constitutes an important issue for news Stance Detection (SD).
Integrating Transformers and Knowledge Graphs for Twitter Stance Detection
Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis.
Incorporating Stock Market Signals for Twitter Stance Detection
Research in stance detection has so far focused on models which leverage purely textual input.
Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus.
Cross-target generalization is a known problem in stance detection (SD), where systems tend to perform poorly when exposed to targets unseen during training.
FBK’s Neural Machine Translation Systems for IWSLT 2016
They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words.
At the Intersection of NLP and Sustainable Development: Exploring the Impact of Demographic-Aware Text Representations in Modeling Value on a Corpus of Interviews
The interviews were later sentence-annotated for Automated User-Perceived Value (UPV) Classification (Conforti et al., 2020), a schema that classifies values expressed by speakers, resulting in a dataset of 5, 333 sentences.
Croissant: A Metadata Format for ML-Ready Datasets
Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point.
Building Representative Corpora from Illiterate Communities: A Reviewof Challenges and Mitigation Strategies for Developing Countries
Most well-established data collection methods currently adopted in NLP depend on the as- sumption of speaker literacy.
Building Representative Corpora from Illiterate Communities: A Review of Challenges and Mitigation Strategies for Developing Countries
Most well-established data collection methods currently adopted in NLP depend on the assumption of speaker literacy.
STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval
We present a new challenging news dataset that targets both stance detection (SD) and fine-grained evidence retrieval (ER).
Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter
We present a new challenging stance detection dataset, called Will-They-Won't-They (WT-WT), which contains 51, 284 tweets in English, making it by far the largest available dataset of the type.
Natural language processing for achieving sustainable development: the case of neural labelling to enhance community profiling
In recent years, there has been an increasing interest in the application of Artificial Intelligence - and especially Machine Learning - to the field of Sustainable Development (SD).
Towards Automatic Fake News Detection: Cross-Level Stance Detection in News Articles
In this paper, we propose to adapt the four-staged pipeline proposed by Zubiaga et al. (2018) for the Rumor Verification task to the problem of Fake News Detection.
Neural Architectures for Open-Type Relation Argument Extraction
In this work, we introduce the task of Open-Type Relation Argument Extraction (ORAE): Given a corpus, a query entity Q and a knowledge base relation (e. g.,"Q authored notable work with title X"), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, e. g. X: the title of a book or a work of art) from the corpus.
