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Found 11 papers, 6 papers with code
Zero-shot Learning for Grapheme to Phoneme Conversion with Language Ensemble
Grapheme-to-Phoneme (G2P) has many applications in NLP and speech fields.
Transformed Protoform Reconstruction
We update their model with the state-of-the-art seq2seq model: the Transformer.
PWESuite: Phonetic Word Embeddings and Tasks They Facilitate
Mapping words into a fixed-dimensional vector space is the backbone of modern NLP.
Mathematically Modeling the Lexicon Entropy of Emergent Language
We formulate a stochastic process, FiLex, as a mathematical model of lexicon entropy in deep learning-based emergent language systems.
Recommendations for Systematic Research on Emergent Language
Emergent language is unique among fields within the discipline of machine learning for its open-endedness, not obviously presenting well-defined problems to be solved.
Modeling Emergent Lexicon Formation with a Self-Reinforcing Stochastic Process
We introduce FiLex, a self-reinforcing stochastic process which models finite lexicons in emergent language experiments.
Quantifying Cognitive Factors in Lexical Decline
Specifically, we propose a variety of psycholinguistic factors -- semantic, distributional, and phonological -- that we hypothesize are predictive of lexical decline, in which words greatly decrease in frequency over time.
Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties
Models pre-trained on multiple languages have shown significant promise for improving speech recognition, particularly for low-resource languages.
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning
We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted.
