Search Results for author:
Found 18 papers, 7 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.
Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment
Allophony refers to the variation in the phonetic realization of a phoneme based on its phonetic environment.
Potential Applications of Artificial Intelligence for Cross-language Intelligibility Assessment of Dysarthric Speech
Purpose: This commentary introduces how artificial intelligence (AI) can be leveraged to advance cross-language intelligibility assessment of dysarthric speech.
Derivational Morphology Reveals Analogical Generalization in Large Language Models
As expected, rule-based and analogical models explain the predictions of GPT-J equally well for adjectives with regular nominalization patterns.
ELCC: the Emergent Language Corpus Collection
The availability of a substantial collection of well-documented emergent language corpora, then, will enable research which can analyze a wider variety of emergent languages, which more effectively uncovers general principles in emergent communication rather than artifacts of particular environments.
XferBench: a Data-Driven Benchmark for Emergent Language
In this paper, we introduce a benchmark for evaluating the overall quality of emergent languages using data-driven methods.
A Review of the Applications of Deep Learning-Based Emergent Communication
Emergent communication, or emergent language, is the field of research which studies how human language-like communication systems emerge de novo in deep multi-agent reinforcement learning environments.
Can Large Language Models Code Like a Linguist?: A Case Study in Low Resource Sound Law Induction
Historical linguists have long written a kind of incompletely formalized ''program'' that converts reconstructed words in an ancestor language into words in one of its attested descendants that consist of a series of ordered string rewrite functions (called sound laws).
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.
