Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring

Unofficial Slides for Introducing Paper.

1. 1/18 1 Unofficial slide by @izuna385
2. 2/18 Summary 2 • Combining both the merits of Bi-encoder and Cross-encoder Bi-encoder Faster Not considering cross-attention Cross-encoder Better performance with cross-attention slow • (Additional:) Pretraining strategy with datasets similar to the downstream tasks.
3. 3/18 3 RQ and Solution • Research Question How to combining both the merits of Bi-encoder and cross-encoder ? • Solution Caching candidates for the attention to contexts.
4. 4/18 Encoder Structure (A.) Bi-encoder [CLS] [CLS] [CLS] Mention
5. 5/18 [CLS] [CLS] [CLS] Mention Entity Encoder Structure (A.) Bi-encoder
6. 6/18 [CLS] [CLS] [CLS] Mention Entity Caching for fast search. Encoder Structure (A.) Bi-encoder
7. 7/18 [CLS] [CLS] [CLS] Caching for fast search. can’t consider cross-attention. Entity Encoder Structure (A.) Bi-encoder
8. 8/18 Encoder Structure (B.) Cross-Encoder • Example: Zero-shot Entity Linking [Logeswaran et al., ACL’19] [CLS] mention context [ENT] input : [Devlin et al., ‘18] L : embedding for indicating mention location ENT candidate entity descriptions
9. 9/18 Encoder Structure (B.) Cross-Encoder • For each generated candidate entity per mention, [CLS] mention context [ENT] candidate entity descriptions input : [Devlin et al., ‘18] L : embedding for indicating mention location [Logeswaran et al., ACL’19] ENT [CLS] scoring
10. 10/18 Encoder Structure (B.) Cross-Encoder • For each generated candidate entity per mention, [CLS] mention context [ENT] input : [Devlin et al., ‘18] L : embedding for indicating mention location [Logeswaran et al., ACL’19] ENT [CLS] scoring Considering mention-entity cross attention. candidate entity descriptions
11. 11/18 Encoder Structure (B.) Cross-Encoder • For each generated candidate entity per mention, [CLS] mention context [ENT] input : [Devlin et al., ‘18] L : embedding for indicating mention location [Logeswaran et al., ACL’19] ENT [CLS] scoring Slow inference per each mention and its candidates. Considering mention-entity cross attention. candidate entity descriptions
12. 12/18 Poly-Encoder • Both and can be cached. à Fast inference. • Attention from candidates. à Extract pertinent parts of the context per candidate.
13. 13/18 • In-batch negative sampling [Henderson et al., ‘17; Gillick et al., ‘18] Context Each gold label Dot one batch … … … In-Batch Training
14. 14/18 • In-batch negative sampling [Henderson et al., ‘17; Gillick et al., ‘18] one batch gold negative negative … In-Batch Training Context Each gold label Dot …
15. 15/18 Results (a.) Effect of negatives in a batch
16. 16/18 Results (b.) Comparison with Bi- / Cross- / Poly- • See Table 4 of the original paper. ・ They also checked the effect of changing pretraining data for BERT.
17. 17/18 Results (c.) Inference Speed Comparison 17
18. 18/18 Conclusions 18