1 code implementation • Findings (ACL) 2022 • Xinjian Li, Florian Metze, David Mortensen, Shinji Watanabe, Alan Black
Grapheme-to-Phoneme (G2P) has many applications in NLP and speech fields.
1 code implementation • 4 Jul 2023 • Young Min Kim, Kalvin Chang, Chenxuan Cui, David Mortensen
We update their model with the state-of-the-art seq2seq model: the Transformer.
1 code implementation • 5 Apr 2023 • Vilém Zouhar, Kalvin Chang, Chenxuan Cui, Nathaniel Carlson, Nathaniel Robinson, Mrinmaya Sachan, David Mortensen
In this work, we develop several novel methods which leverage articulatory features to build phonetically informed word embeddings, and present a set of phonetic word embeddings to encourage their community development, evaluation and use.
1 code implementation • 28 Nov 2022 • Brendon Boldt, David Mortensen
We formulate a stochastic process, FiLex, as a mathematical model of lexicon entropy in deep learning-based emergent language systems.
1 code implementation • 22 Jun 2022 • Brendon Boldt, David Mortensen
We introduce FiLex, a self-reinforcing stochastic process which models finite lexicons in emergent language experiments.
no code implementations • 22 Jun 2022 • Brendon Boldt, David Mortensen
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.
1 code implementation • 12 Oct 2021 • David Francis, Ella Rabinovich, Farhan Samir, David Mortensen, Suzanne Stevenson
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.
no code implementations • 4 Apr 2021 • Kathleen Siminyu, Xinjian Li, Antonios Anastasopoulos, David Mortensen, Michael R. Marlo, Graham Neubig
Models pre-trained on multiple languages have shown significant promise for improving speech recognition, particularly for low-resource languages.
no code implementations • NAACL 2016 • Yulia Tsvetkov, Sunayana Sitaram, Manaal Faruqui, Guillaume Lample, Patrick Littell, David Mortensen, Alan W. black, Lori Levin, Chris Dyer
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.