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Found 10 papers, 4 papers with code
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese
The results demonstrate that our test sets are capable of measuring the quality of vector space models and can provide a holistic view of the model’s ability to capture syntactic and semantic information.
Period Classification in Chinese Historical Texts
In this study, we study language change in Chinese Biji by using a classification task: classifying Ancient Chinese texts by time periods.
How “Loco” Is the LOCO Corpus? Annotating the Language of Conspiracy Theories
Conspiracy theories have found a new channel on the internet and spread by bringing together like-minded people, thus functioning as an echo chamber.
Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings
Inducing semantic representations directly from speech signals is a highly challenging task but has many useful applications in speech mining and spoken language understanding.
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference
These results, however, come with important caveats: cross-lingual models often perform best when trained on a mixture of English and high-quality monolingual NLI data (OCNLI), and are often hindered by automatically translated resources (XNLI-zh).
Offensive Language Detection Using Brown Clustering
In this study, we investigate the use of Brown clustering for offensive language detection.
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
Ensemble Methods to Distinguish Mainland and Taiwan Chinese
This paper describes the IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese.
UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs
This paper describes the UM-IU@LING's system for the SemEval 2019 Task 6: OffensEval.
