Closed Domain Semantic Question Answering System as a Use Case of Transformer-Based BERT Models
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View abstract on PubMed
Summary
This summary is machine-generated.Google-BERT excels at question answering (QA) by understanding synonyms but struggles with spelling errors. This research compares transformer models for knowledge extraction from text.
Area Of Science
- Natural Language Processing
- Artificial Intelligence
- Machine Learning
Background
- Question Answering Systems (QAS) are crucial for extracting knowledge from text.
- Advancements in transformer models offer new possibilities for direct-answer QA tasks.
Purpose Of The Study
- To evaluate and compare the inference capabilities of pre-trained transformer models for QA.
- To assess performance against a traditional TF-IDF model.
Main Methods
- Utilized a Semantic Closed-domain QA (SCD-QA) dataset with factoid and non-factoid questions.
- Compared Google-BERT, DistilBERT, and RoBERTa against a TF-IDF model on the SQuAD dataset.
- Measured Exact Match (EM) scores and latency.
Main Results
- Google-BERT achieved the highest performance with a 90.0 EM score and 1.27s latency.
- Transformer models demonstrated strong understanding of semantic meaning, including synonyms.
- Performance degraded on questions with spelling errors, highlighting sensitivity to misspellings.
Conclusions
- Google-BERT is a highly effective model for QA tasks, particularly with semantic variations.
- Further improvements in preprocessing are needed to address sensitivity to spelling errors.
- The study validates the effectiveness of transformer models in knowledge extraction and QA.
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