# Word-specific tonal realizations in Mandarin

arXiv: https://arxiv.org/abs/2405.07006 · HTML: https://mlsanity.com/paper/2405.07006/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 160 references that are in the map.
- First posted on arXiv 11 May 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Probing Pretrained Language Models for Lexical Semantics](https://mlsanity.com/paper/2010.05731/) (2020)
- [WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach](https://mlsanity.com/paper/2104.01767/) (2021)
- [Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Senses](https://mlsanity.com/paper/2104.14694/) (2021)
- [Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space](https://mlsanity.com/paper/1504.06654/) (2015)
- [Smoothing parameter and model selection for general smooth models](https://mlsanity.com/paper/1511.03864/) (2015)
- [Modeling morphology with Linear Discriminative Learning: considerations and design choices](https://mlsanity.com/paper/2106.07936/) (2021)
- [Vector Space Morphology with Linear Discriminative Learning](https://mlsanity.com/paper/2107.03950/) (2021)
- [Prosody leaks into the memories of words](https://mlsanity.com/paper/2005.14716/) (2020)
- [A note on the modeling of the effects of experimental time in psycholinguistic experiments](https://mlsanity.com/paper/2105.13786/) (2021)
- [How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning](https://mlsanity.com/paper/2207.00430/) (2022)
- [Frequency effects in Linear Discriminative Learning](https://mlsanity.com/paper/2306.11044/) (2023)

## Built on by (most cited first, 11 of 11)

- [The realization of tones in spontaneous spoken Taiwan Mandarin: a corpus-based survey and theory-driven computational modeling](https://mlsanity.com/paper/2503.23163/) (2025)
- [Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model](https://mlsanity.com/paper/2410.04259/) (2024)
- [A new kid on the block: Distributional semantics predicts the word-specific tone signatures of monosyllabic words in conversational Taiwan Mandarin](https://mlsanity.com/paper/2511.17337/) (2025)
- [Form and meaning co-determine the realization of tone in Taiwan Mandarin spontaneous speech: the case of T2-T3 and T3-T3 tone sandhi](https://mlsanity.com/paper/2408.15747/) (2024)
- [A corpus-based investigation of pitch contours of monosyllabic words in conversational Taiwan Mandarin](https://mlsanity.com/paper/2409.07891/) (2024)
- [Using embeddings to predict spoken word duration and pitch in Mandarin monosyllabic words](https://mlsanity.com/paper/2607.02002/) (2026)
- [An experimental and computational study of an Estonian single-person word naming](https://mlsanity.com/paper/2509.03143/) (2025)
- [Polish phonology and morphology through the lens of distributional semantics](https://mlsanity.com/paper/2604.00174/) (2026)
- [Phonetic and semantic analyses of spoken corpora of Beijing and Taiwan Mandarin indicate that the neutral tone is a lexical tone](https://mlsanity.com/paper/2606.26360/) (2026)
- [Word meaning co-determines vowel-inherent spectral change. A corpus-based investigation of conversational Mandarin](https://mlsanity.com/paper/2607.21391/) (2026)
- [Letters hide the truth from our eyes: English homophones have meaningfully different phonetic realizations](https://mlsanity.com/paper/2608.26749/) (2026)

## Abstract (arXiv)

The pitch contours of Mandarin two-character words are generally understood as being shaped by the underlying tones of the constituent single-character words, in interaction with articulatory constraints imposed by factors such as speech rate, co-articulation with adjacent tones, segmental make-up, and predictability. This study shows that tonal realization is also partially determined by words' meanings. We first show, on the basis of a corpus of Taiwan Mandarin spontaneous conversations, using a generalized additive regression model, and focusing on the rise-fall tone pattern, that after controlling for effects of speaker and context, word type is a stronger predictor of tonal realization than all the previously established word-form related predictors combined. Importantly, the addition of information about meaning in context improves prediction accuracy even further. We then proceed to show, using computational modeling with context-specific word embeddings, that token-specific pitch contours predict word type with 50% accuracy on held-out data, and that context-sensitive, token-specific embeddings can predict the shape of pitch contours with 40% accuracy. These accuracies, which are an order of magnitude above chance level, suggest that the relation between words' pitch contours and their meanings are sufficiently strong to be potentially functional for language users. The theoretical implications of these empirical findings are discussed.