Zero-Shot Entity Linking by Reading Entity Descriptions

supplement slides.
Other EL paper slides and summaries:
https://github.com/izuna385/EntityLinking_RecentTrend

Transcript

1. 1/31 1 Zero-Shot Entity Linking by Reading Entity Descriptions (supplement slides) slides by @izuna385 Unofficial
2. 2/31 Previous Entity Linking(EL) 2 1. Prepare Mention/Context vector 2. Candidate generation 3. Linking 0. Learn/prepare Entity(inKB) representation
3. 3/31 Previous Entity Linking 1. Prepare Mention/Context vector 0. Learn/prepare Entity(inKB) representation 2. Candidate generation 3. Linking A. In-domain limited B. mention-entity cross attention is not considered
4. 4/31 A. In-domain limited EL Problems 4 • Although recent Wikipedia-based EL researches saw great success, these successes were, partly due to
   massive mention-entity pair (1B~)
   Substantial alias table for candidate generation. Alias table is also created from abundant hyperlinks. • Under specific domains, these annotations are limited and expensive. • “Therefore, we need entity linking systems that can generalize to unseen specialized entities.”
5. 5/31 Bronchopulmonary Dysplasia was first described by Northway as a lung injury. B. Mention-entity cross attention was not considered.
   mention/context encoding Mention Encoder mention
     candidate entity generation for one mention  predict entity by score function  • Previous : encoded mention vs encoded candidate entities.(See ) Dysplasia Pulmonary BPdysplasia … candidate entities
   encode candidate entities using its descriptions, structures, etc. Entity Encoder
   
6. 6/31 Bronchopulmonary Dysplasia was first described by Northway as a lung injury. B. Mention-entity cross attention was not considered.
   mention/context encoding Mention Encoder mention
     candidate entity generation for one mention  predict entity by score function  • Previous : encoded mention vs encoded candidate entities. Dysplasia Pulmonary BPdysplasia … candidate entities
   encode candidate entities using its descriptions , structures, etc. Entity Encoder mention–description interaction was ignored.
   
7. 7/31 Their Contributions Proposing Zero-shot EL Showing context-description attention is crucial for EL. Proposing DA-pretrain for EL. (Details are later described.) (A) for in-domain limited EL, (B) for mention-entity interaction
8. 8/31 Pre-assumption ① : Entity dictionary • They first presupposes only entity dictionary. : its descriptions : entity
9. 9/31 Pre-assumption ② : Worlds( W ) • Each world W has its own : its descriptions : entity
   
   : documents belonging to W
   : labeled spans in , annotated by
10. 10/31 Pre-assumption ② : Worlds( W ) • Each world W has its own : its descriptions : entity
    
    : documents belonging to W
    : labeled spans in , annotated by constructed from pages
11. 11/31 Pre-assumption ② : Worlds( W ) = Worlds( W ) : its description : : entity : mention (documents) : constructed from collections meninblack.fandom.com/wiki/Frank_the_Pug
12. 12/31 Pre-assumption ② : Worlds( W ) = Worlds( W ) : its description : : entity : mention (documents) : constructed from collections meninblack.fandom.com/wiki/Frank_the_Pug constructed from
13. 13/31 Pre-assumption ② : Worlds( W ) : its descriptions : entity
    
    : documents belonging to W
    : labeled spans in , annotated by … …
14. 14/31 Pre-assumption ② : Worlds( W ) : its descriptions : entity
    
    : documents belonging to W
    : labeled spans in , annotated by … … This is for “Entity Linking”
15. 15/31 … … Pre-assumption ② : Worlds( W ) : its descriptions : entity
    
    : documents belonging to W
    : labeled spans in , annotated by … … down-sampled down-sampled Another documents are preserved as corpus for Domain-adaptive pre-training.
16. 16/31 Previous pretraining LM vs DA pretraining LM • Task-adaptive pretraining Learning with src + tgt corpus à finetune with src corpus for solving specific task.(e.g. NER) (tgt corpus supposed to be small.) LM : Language Model DA: Domain adaptive src : source tgt : target
17. 17/31 Previous pretraining LM vs DA pretraining LM • Task-adaptive pretraining Learning with src + tgt corpus à finetune with src corpus for solving specific task.(e.g. NER) (tgt corpus supposed to be small.) • Open-corpus pre-training Learning with massive src + tgt corpus. (e.g. ELMo, BERT, SciBERT,…) LM : Language Model DA: Domain adaptive src : source tgt : target
18. 18/31 Previous pretraining LM vs DA pretraining LM • Task-adaptive pretraining Learning with src + tgt corpus à finetune with src corpus for solving specific task.(e.g. NER) (tgt corpus supposed to be small.) • Open-corpus pre-training Learning with massive src + tgt corpus. (e.g. ELMo, BERT, SciBERT,…) • Domain-adaptive pre-training(DAP) (proposed) pre-trained only on the tgt corpus. LM : Language Model DA: Domain adaptive src : source tgt : target
19. 19/31 When and Why DAP?
20. 20/31 How to prepare src/tgt corpus for fine-tuning LM?
21. 21/31 Their Contributions Proposing Zero-shot EL Showing context-description attention is crucial for EL. Proposing DA-pretrain for EL. (Details are later described.) (A) for in-domain limited EL, (B) for mention-entity interaction
22. 22/31 (B) Context-description interaction model • For each generated candidate entity per mention, (i)Full-transformer model (proposed) [CLS] mention context [SEP] entity descriptions input : [Devlin et al., ‘18] L : embedding for indicating mention location
23. 23/31 (B) Context-description interaction model • For each generated candidate entity per mention, (i)Full-transformer model (proposed) output : [Devlin et al., ‘18] [CLS](= ) • Scoring candidates( s) by : learned vector
24. 24/31 (B) Context-description interaction model • For each generated candidate entity per mention, (ii)Pool-transformer model (for comparison) output : [Devlin et al., ‘18] [CLS](= ) Scoring [CLS](= ) [CLS] [CLS]entity descriptions [SEP] [SEP] mention context
25. 25/31 (B) Context-description interaction model • For each generated candidate entity per mention, (ii)Cand-Pool-transformer model (for comparison) [Devlin et al., ‘18] [CLS] [CLS] [CLS]entity descriptions [SEP] [SEP] mention context input is same
26. 26/31 (B) Context-description interaction model • For each generated candidate entity per mention, (iii)Cand-Pool-transformer model (for comparison) [Devlin et al., ‘18] [CLS] [CLS] [CLS]entity descriptions [SEP] [SEP] mention context Using d att to mention
27. 27/31 (B) Context-description interaction model • For each generated candidate entity per mention, (iii)Cand-Pool-transformer model (for comparison) [Ganea and Hofmann, ‘17] K : candidates per mention Scoring
28. 28/31 Result for changing resource for pretraining LM • NOTE: this is not DAP.
29. 29/31 (A): Is DAP strategy effective for DA? Coronation street Muppets Ice hockey Elder scrolls : Wikipedia + Book corpus : 8 worlds, apart from dev and test DAP is effective.
30. 30/31 (B): Is mention - entity description attention powerful? Mention entity cross-attention is effective.
31. 31/31 Conclusions / Their Contributions Proposing Zero-shot EL Showing context-description attention is crucial for EL. Proposing DA-pretrain for EL. (Details are later described.) (A) for in-domain limited EL, (B) for mention-entity interaction