Papers with neural sentence embeddings
SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features (2022.aacl-main)
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| Challenge: | Abstract Meaning Representation (S3BERT) embeddings are composed of explainable sub-embeddings that emphasize various sentence meaning features. |
| Approach: | They propose to induce Semantically Structured Sentence BERT embeddings (S3BERT) that emphasize various sentence meaning features. |
| Outcome: | The proposed model shows high correlation to human similarity ratings, but lacks interpretability. |
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems (N19-1)
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Stefan Larson, Anish Mahendran, Andrew Lee, Jonathan K. Kummerfeld, Parker Hill, Michael A. Laurenzano, Johann Hauswald, Lingjia Tang, Jason Mars
| Challenge: | Existing methods to detect outliers in text have been neglected in NLP . outlier detection is a problem in dialog systems where text is often no more than a few sentences in length. |
| Approach: | They propose a technique that uses sentence embeddings to detect outliers in short texts using neural sentence embeds and distance-based outlier detection. |
| Outcome: | The proposed technique detects outliers in a corpus of short texts while generating highly diverse corpora that produce more robust intent classification and slot-filling models. |