As the reach of large language models (LMs) expands globally, their ability to cater to
diverse cultural contexts becomes crucial. Despite advancements in multilingual …

We present a number of novel contributions to the multiple-source adaptation problem. We
derive new normalized solutions with strong theoretical guarantees for the cross-entropy …

Although large language models (LLMs) have been touted for their ability to generate
natural-sounding text, there are growing concerns around possible negative effects of LLMs …

Abstract Machine learning, including neural network techniques, have been applied to
virtually every domain in natural language processing. One problem that has been …

The grammar, or syntax, of human language is typically understood in terms of abstract
hierarchical structures. However, theories of language processing that emphasize …

This paper presents a challenge to the community: given a large corpus of written text
aligned to its normalized spoken form, train an RNN to learn the correct normalization …

This paper provides an analysis of several practical issues related to the theory and
implementation of Grapheme-to-Phoneme (G2P) conversion systems utilizing the Weighted …

We describe the design and findings of the SIGMORPHON 2020 shared task on multilingual
grapheme-to-phoneme conversion. Participants were asked to submit systems which take in …

This paper describes the Kestrel text normalization system, a component of the Google text-
to-speech synthesis (TTS) system. At the core of Kestrel are text-normalization grammars …

We aim to build a vast database (up to 9 million individuals) from the handwritten tabular
nominal census of Paris of 1926, 1931 and 1936, each composed of about 100,000 …