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| 설명 가능한 질의응답× | 문장 임베딩× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2016–2020 | 2015–2019 |
| 창시자≠ | Community (DeYoung et al.; Rajpurkar et al.) | Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019) |
| 유형≠ | Interpretable NLP pipeline | Representation learning / embedding |
| 원전≠ | DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020). ERASER: A Benchmark to Evaluate Rationalized NLP Models. In Proceedings of ACL 2020, pp. 4443–4458. DOI ↗ | Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3980–3990. DOI ↗ |
| 별칭 | XQA, interpretable QA, transparent question answering, rationale-based QA | sentence vectors, sentence representations, SBERT, semantic sentence encoding |
| 관련≠ | 5 | 4 |
| 요약≠ | Explainable Question Answering (XQA) combines neural reading-comprehension models — typically BERT-family transformers — with interpretability methods such as rationale extraction, attention visualization, LIME, or SHAP to reveal why the model selected a particular answer span. The goal is not just accuracy but trustworthy, auditable reasoning that users and domain experts can inspect and verify. | Sentence Embeddings convert a sentence or short text into a single fixed-length dense vector that captures its semantic meaning. These vectors allow downstream tasks — semantic similarity, clustering, retrieval, and classification — to operate on numerical representations instead of raw text, making them one of the most versatile building blocks in modern NLP pipelines. |
| ScholarGate데이터셋 ↗ |
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