# A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances

arXiv: https://arxiv.org/abs/2403.14808 · HTML: https://mlsanity.com/paper/2403.14808/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 19 of 19)

- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [AudioPaLM: A Large Language Model That Can Speak and Listen](https://mlsanity.com/paper/2306.12925/) (2023)
- [Seamless: Multilingual Expressive and Streaming Speech Translation](https://mlsanity.com/paper/2312.05187/) (2023)
- [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://mlsanity.com/paper/2202.06935/) (2022)
- [Generative Spoken Dialogue Language Modeling](https://mlsanity.com/paper/2203.16502/) (2022)
- [Beyond BLEU: Training Neural Machine Translation with Semantic Similarity](https://mlsanity.com/paper/1909.06694/) (2019)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [Generalization Ability of MOS Prediction Networks](https://mlsanity.com/paper/2110.02635/) (2021)
- [The VoiceMOS Challenge 2022](https://mlsanity.com/paper/2203.11389/) (2022)
- [On the Utility of Self-supervised Models for Prosody-related Tasks](https://mlsanity.com/paper/2210.07185/) (2022)
- [Learning Semantic Textual Similarity from Conversations](https://mlsanity.com/paper/1804.07754/) (2018)
- [SpeechLMScore: Evaluating speech generation using speech language model](https://mlsanity.com/paper/2212.04559/) (2022)
- [BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric](https://mlsanity.com/paper/2212.08486/) (2022)
- [Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop](https://mlsanity.com/paper/2011.05533/) (2020)
- [A Holistic Cascade System, benchmark, and Human Evaluation Protocol for Expressive Speech-to-Speech Translation](https://mlsanity.com/paper/2301.10606/) (2023)
- [Assessing Evaluation Metrics for Speech-to-Speech Translation](https://mlsanity.com/paper/2110.13877/) (2021)
- [Automatic Evaluation of Turn-taking Cues in Conversational Speech Synthesis](https://mlsanity.com/paper/2305.17971/) (2023)
- [Towards cross-language prosody transfer for dialog](https://mlsanity.com/paper/2307.04123/) (2023)
- [A Textless Metric for Speech-to-Speech Comparison](https://mlsanity.com/paper/2210.11835/) (2022)

## Built on by (most cited first, 2 of 2)

- [Which Prosodic Features Matter Most for Pragmatics?](https://mlsanity.com/paper/2408.13240/) (2024)
- [Towards Precision Characterization of Communication Disorders using Models of Perceived Pragmatic Similarity](https://mlsanity.com/paper/2409.09170/) (2024)

## Abstract (arXiv)

Automatic measures of similarity between utterances are invaluable for training speech synthesizers, evaluating machine translation, and assessing learner productions. While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity. To enable the training of such measures, we developed the first collection of human judgments of pragmatic similarity between utterance pairs. Each pair consisting of an utterance extracted from a recorded dialog and a re-enactment of that utterance. Re-enactments were done under various conditions designed to create a variety of degrees of similarity. Each pair was rated on a continuous scale by 6 to 9 judges. The average inter-judge correlation was as high as 0.72 for English and 0.66 for Spanish. We make this data available at https://github.com/divettemarco/PragSim .