Summary of Efficient Data Selection Employing Semantic Similarity-based Graph Structures For Model Training, by Roxana Petcu and Subhadeep Maji
Efficient data selection employing Semantic Similarity-based Graph Structures for model training
by Roxana Petcu, Subhadeep Maji
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces SeSaME (Semantics for data SAliency in Model performance Estimation), an efficient data sampling mechanism that leverages textual information to accurately capture model performance. The approach is demonstrated in the context of low-resource automated speech recognition (ASR) models, which rely heavily on text-to-speech (TTS) calls using augmented data. SeSaME employs semantic similarity-based graph structures and discrete ASR information from homophilous neighborhoods through message passing to categorize new incoming data points into speech recognition difficulty buckets. The results show reliable projections of ASR performance with a 93% accuracy increase compared to random predictions, highlighting the impact of textual representations in speech models. Additionally, experiments demonstrate the benefits and challenges of using ASR information on incoming data to fine-tune the model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making computer programs that can understand human language better. Right now, these programs need a lot of data to learn and get accurate. The authors propose a new way to pick the right data for these programs, called SeSaME. They test it on a specific problem: getting computers to recognize spoken words. Using this method, they can predict how well the program will do with just a few pieces of information from the spoken word. This is important because it could help make language understanding programs more efficient and accurate. |
Keywords
* Artificial intelligence * Language understanding * Semantics
