# Using Pause Information for More Accurate Entity Recognition

arXiv: https://arxiv.org/abs/2109.13222 · HTML: https://mlsanity.com/paper/2109.13222/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [From Audio to Semantics: Approaches to end-to-end spoken language understanding](https://mlsanity.com/paper/1809.09190/) (2018)
- [Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation](https://mlsanity.com/paper/2005.08575/) (2020)
- [Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition](https://mlsanity.com/paper/1903.12306/) (2019)
- [SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering](https://mlsanity.com/paper/1910.11559/) (2019)
- [BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language Recognition](https://mlsanity.com/paper/1907.00457/) (2019)
- [Voice trigger detection from LVCSR hypothesis lattices using bidirectional lattice recurrent neural networks](https://mlsanity.com/paper/2003.00304/) (2020)
- [Noise Robust Named Entity Understanding for Voice Assistants](https://mlsanity.com/paper/2005.14408/) (2020)
- [SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding](https://mlsanity.com/paper/2010.02295/) (2020)
- [Sound-Word2Vec: Learning Word Representations Grounded in Sounds](https://mlsanity.com/paper/1703.01720/) (2017)
- [Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant](https://mlsanity.com/paper/1909.09143/) (2019)

## Built on by (most cited first, 1 of 1)

- [STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants](https://mlsanity.com/paper/2310.16990/) (2023)

## Abstract (arXiv)

Entity tags in human-machine dialog are integral to natural language understanding (NLU) tasks in conversational assistants. However, current systems struggle to accurately parse spoken queries with the typical use of text input alone, and often fail to understand the user intent. Previous work in linguistics has identified a cross-language tendency for longer speech pauses surrounding nouns as compared to verbs. We demonstrate that the linguistic observation on pauses can be used to improve accuracy in machine-learnt language understanding tasks. Analysis of pauses in French and English utterances from a commercial voice assistant shows the statistically significant difference in pause duration around multi-token entity span boundaries compared to within entity spans. Additionally, in contrast to text-based NLU, we apply pause duration to enrich contextual embeddings to improve shallow parsing of entities. Results show that our proposed novel embeddings improve the relative error rate by up to 8% consistently across three domains for French, without any added annotation or alignment costs to the parser.