# Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts

arXiv: https://arxiv.org/abs/2106.07794 · HTML: https://mlsanity.com/paper/2106.07794/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Linguistic Knowledge and Transferability of Contextual Representations](https://mlsanity.com/paper/1903.08855/) (2019)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://mlsanity.com/paper/1812.11760/) (2018)
- [Deep RNNs Encode Soft Hierarchical Syntax](https://mlsanity.com/paper/1805.04218/) (2018)
- [Linguistic Profiling of a Neural Language Model](https://mlsanity.com/paper/2010.01869/) (2020)
- [Disfluencies and Human Speech Transcription Errors](https://mlsanity.com/paper/1904.04398/) (2019)
- [Neural Constituency Parsing of Speech Transcripts](https://mlsanity.com/paper/1904.08535/) (2019)
- [On the Role of Style in Parsing Speech with Neural Models](https://mlsanity.com/paper/2010.04288/) (2020)

## Built on by (most cited first, 3 of 3)

- [Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken Sentences](https://mlsanity.com/paper/2303.08809/) (2023)
- [Audio-Visual Neural Syntax Acquisition](https://mlsanity.com/paper/2310.07654/) (2023)
- [Structured Tree Alignment for Evaluation of (Speech) Constituency Parsing](https://mlsanity.com/paper/2402.13433/) (2024)

## Abstract (arXiv)

This work explores constituency parsing on automatically recognized transcripts of conversational speech. The neural parser is based on a sentence encoder that leverages word vectors contextualized with prosodic features, jointly learning prosodic feature extraction with parsing. We assess the utility of the prosody in parsing on imperfect transcripts, i.e. transcripts with automatic speech recognition (ASR) errors, by applying the parser in an N-best reranking framework. In experiments on Switchboard, we obtain 13-15% of the oracle N-best gain relative to parsing the 1-best ASR output, with insignificant impact on word recognition error rate. Prosody provides a significant part of the gain, and analyses suggest that it leads to more grammatical utterances via recovering function words.