# Helpful Neighbors: Leveraging Neighbors in Geographic Feature Pronunciation

arXiv: https://arxiv.org/abs/2210.10200 · HTML: https://mlsanity.com/paper/2210.10200/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Deep context: end-to-end contextual speech recognition](https://mlsanity.com/paper/1808.02480/) (2018)
- [Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion](https://mlsanity.com/paper/1506.00196/) (2015)
- [Massively Multilingual Neural Grapheme-to-Phoneme Conversion](https://mlsanity.com/paper/1708.01464/) (2017)
- [g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset](https://mlsanity.com/paper/2004.03136/) (2020)
- [Glyph-aware Embedding of Chinese Characters](https://mlsanity.com/paper/1709.00028/) (2017)
- [Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models](https://mlsanity.com/paper/1610.06540/) (2016)
- [Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural Networks](https://mlsanity.com/paper/1912.09913/) (2019)
- [Text normalization using memory augmented neural networks](https://mlsanity.com/paper/1806.00044/) (2018)
- [Polyphone Disambiguation in Mandarin Chinese with Semi-Supervised Learning](https://mlsanity.com/paper/2102.00621/) (2021)
- [Neural text normalization leveraging similarities of strings and sounds](https://mlsanity.com/paper/2011.02173/) (2020)

## Abstract (arXiv)

If one sees the place name Houston Mercer Dog Run in New York, how does one know how to pronounce it? Assuming one knows that Houston in New York is pronounced "how-ston" and not like the Texas city, then one can probably guess that "how-ston" is also used in the name of the dog park. We present a novel architecture that learns to use the pronunciations of neighboring names in order to guess the pronunciation of a given target feature. Applied to Japanese place names, we demonstrate the utility of the model to finding and proposing corrections for errors in Google Maps. To demonstrate the utility of this approach to structurally similar problems, we also report on an application to a totally different task: Cognate reflex prediction in comparative historical linguistics. A version of the code has been open-sourced (https://github.com/google-research/google-research/tree/master/cognate_inpaint_neighbors).