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Found 10 papers, 4 papers with code
Challenges of Building Domain-Specific Parallel Corpora from Public Administration Documents
The collected LRs were used for the development of neural MT engines in order to verify the quality of the resources.
Synthetic, yet natural: Properties of WordNet random walk corpora and the impact of rare words on embedding performance
Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge.
Idioms, Probing and Dangerous Things: Towards Structural Probing for Idiomaticity in Vector Space
The goal of this paper is to learn more about how idiomatic information is structurally encoded in embeddings, using a structural probing method.
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
Probing Taxonomic and Thematic Embeddings for Taxonomic Information
Modelling taxonomic and thematic relatedness is important for building AI with comprehensive natural language understanding.
Probing with Noise: Unpicking the Warp and Weft of Embeddings
Improving our understanding of how information is encoded in vector space can yield valuable interpretability insights.
Is it worth it? Budget-related evaluation metrics for model selection
Creating a linguistic resource is often done by using a machine learning model that filters the content that goes through to a human annotator, before going into the final resource.
Examining a hate speech corpus for hate speech detection and popularity prediction
As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection.
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems.
Fine-grained human evaluation of neural versus phrase-based machine translation
We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems' outputs.
