Zero-shot Entity Linking with Dense Entity Retrieval (Unofficial slides) and Entity Linking future directions

supplement for journal club
Entity Linking future directions are also listed.

Transcript

1. 1/42 1 Zero-Shot Entity Linking with Dense Entity Retrieval unofficial slides by @izuna385
2. 2/42 Previous Entity Linking(EL) 2 1. Prepare Mention/Context vector 2. Candidate generation 3. Linking 0. Learn/prepare Entity(inKB) representation
3. 3/42 Previous Entity Linking 1. Prepare Mention/Context vector 0. Learn/prepare Entity(inKB) representation 2. Candidate generation 3. Linking A. In-domain limited
4. 4/42 Previous Entity Linking 1. Prepare Mention/Context vector 0. Learn/prepare Entity(inKB) representation 2. Candidate generation 3. Linking A. In-domain limited B. Only surface-based candidate generation
5. 5/42 Previous Entity Linking 1. Prepare Mention/Context vector 0. Learn/prepare Entity(inKB) representation 2. Candidate generation 3. Linking A. In-domain limited C. mention-entity cross attention is not considered B. Only surface-based candidate generation
6. 6/42 A. In-domain limited EL Problems 6 • Wikipedia-based EL successes were, partly due to
   massive mention-entity pair (1B~)
   Substantial alias table for candidate generation.
7. 7/42 A. In-domain limited EL Problems 7 • Wikipedia-based EL successes were, partly due to
   massive mention-entity pair (1B~)
   Substantial alias table for candidate generation. • Under specific domains, these annotations are limited and expensive. • “Therefore, we need entity linking systems that can generalize to unseen specialized entities.”
8. 8/42 B. Surface-based candidate generation • Generation Failure examples Mention in document: “ALL” Generated Candidates: "All Sites", "All of the Time", “Alleviation” Gold entity: “Acute lymphocytic leukemia" Abbreviation
9. 9/42 B. Surface-based candidate generation • Generation Failure examples Mention in document: “ALL” Generated Candidates: "All Sites", "All of the Time", “Alleviation” Gold entity: “Acute lymphocytic leukemia" Mention in document: “Giα” Generated Candidates: "Gin", "Gibraltar", “Gill structure” Gold entity: “GTP-Binding Protein alpha Subunit, Gi" Abbreviation Common name(
   ) mention
10. 10/42 B. Surface-based candidate generation • Generation Failure examples Mention in document: “ALL” Generated Candidates: "All Sites", "All of the Time", “Alleviation” Gold entity: “Acute lymphocytic leukemia" Mention in document: “Giα” Generated Candidates: "Gin", "Gibraltar", “Gill structure” Gold entity: “GTP-Binding Protein alpha Subunit, Gi" Abbreviation Common name() mention • Mention’s orthographical variants(
    ).
11. 11/42 Bronchopulmonary Dysplasia was first described by Northway as a lung injury. C. 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
    
12. 12/42 Bronchopulmonary Dysplasia was first described by Northway as a lung injury. C. 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
    Fixed vector comparison.
13. 13/42 Bronchopulmonary Dysplasia was first described by Northway as a lung injury. C. 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.
    
14. 14/42 Baselines / Their contributions • Baseline Zero-Shot Entity Linking by Reading Entity Descriptions [Logeswaran et al., ACL’19]
15. 15/42 Baselines / Their contributions • Baseline Zero-Shot Entity Linking by Reading Entity Descriptions [Logeswaran et al., ACL’19] • Main contribution Logeswaran et al. used surface-based CG
16. 16/42 Baselines / Their contributions • Baseline Zero-Shot Entity Linking by Reading Entity Descriptions [Logeswaran et al., ACL’19] • Main contribution Logeswaran et al. used surface-based CG à Change this to emb.-search and show higher recall.
17. 17/42 Baselines / Their contributions • Baseline Zero-Shot Entity Linking by Reading Entity Descriptions [Logeswaran et al., ACL’19] • Main contribution Logeswaran et al. used surface-based CG à Change this to emb.-search and show higher recall. • Sub contribution Logeswaran et al. used slow cross-encoder. (details in later)
18. 18/42 Baselines / Their contributions • Baseline Zero-Shot Entity Linking by Reading Entity Descriptions [Logeswaran et al., ACL’19] • Main contribution Logeswaran et al. used surface-based CG à Change this to emb.-search and show higher recall. • Sub contribution Logeswaran et al. used slow cross-encoder. (details in later) à Compare this with fast bi-encoder [Humeau et al., ICLR’20 poster].
19. 19/42 Encoder structure (A.) Bi-encoder [Humeau et al., ICLR’20] [CLS] [CLS] [CLS] Mention
20. 20/42 Encoder structure (A.) Bi-encoder [Humeau et al., ICLR’20] [CLS] [CLS] [CLS] Mention Entity Caching for fast search.
21. 21/42 Encoder structure (A.) Bi-encoder [Humeau et al., ICLR’20] [CLS] [CLS] [CLS] Caching for fast search. can’t consider cross-attention. Entity
22. 22/42 Encoder structure (B.) Cross-Encoder • For each generated candidate entity per mention, consider mention-entity cross attention. [Devlin et al., ‘18] [Logeswaran et al., ACL’19]
23. 23/42 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 candidate entity descriptions
24. 24/42 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
25. 25/42 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
26. 26/42 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
27. 27/42 Optimization and Evaluation • Optimization : Based on gold / random negative sampling
28. 28/42 Optimization and Evaluation • Optimization : Based on gold / random negative sampling • Evaluation
    Recall@64 : is gold available @ top64 scored entities?
    Accuracy : is top1 scored entity gold?
29. 29/42 Optimization and Evaluation • Optimization : Based on gold / random negative sampling • Evaluation
    Recall@64 : is gold available @ top64 scored entities?
    Accuracy : is top1 scored entity gold?
    Normalized acc. : evaluation only mentions which succeeded in CG.
30. 30/42 Result (1) BM25 vs. Bi-encoder brute-force • @Zero-shot dataset. cross-encoder bi-encoder + BT BT : Brute-force search
31. 31/42 Result (1) BM25 vs. Bi-encoder brute-force Both used cross-encoder. cross-encoder bi-encoder + BT • @Zero-shot dataset. BT : Brute-force search
32. 32/42 Result (2) Normalized acc. evaluation. Both used cross-encoder. • @Zero-shot dataset.
33. 33/42 Result (3) Bi-encoder vs. cross-encoder • @TAC-KBP10 dataset. • Bi-encoder : fast but can’t consider mention-entity cross attention. Cross-encoder : slow but consider
    
34. 34/42 Result (3) Bi-encoder vs. cross-encoder • @TAC-KBP10 dataset. • Bi-encoder : fast but can’t consider mention-entity cross attention. Cross-encoder : slow but consider
    cross-encoder + BT bi-encoder + BT simple-encoder + BT BT : Brute-force search
35. 35/42 Conclusions • Fast and scalable EL model for New/General Domain. • Even cross-att. is removed, fast EL model has good acc.
36. 36/42 Entity Linking future directions(1) • Distant / No-label situations.
37. 37/42 Entity Linking future directions(1) • Distant / No-label situations. [Le and Titov, ACL’19a] Surface-match + Multi-instance Learning. [Le and Titov, ACL’19b] Spacy +
    + Wikipedia hyperlinks edge/statistics
38. 38/42 Entity Linking future directions(2) • Improving Entity representations.
39. 39/42 Entity Linking future directions(2) • Improving Entity representations. Yes Require “entity-span” annotations? Use relations? Yes No Use relation? JointEnt [Yamada et al., ACL ’17] KnowBert [Peters, et al, EMNLP ’19] (Indirectly annotated data used) KEPLER [Wang et al., ‘Nov 19] No Yes No DEER [Gillick et al., CoNLL ’19] ERNIE [Zhang et al., ACL ’19] BertEnt [Yamada et al., ’19] EntEval [Chen et al., EMNLP’19] WKLM [Xiong et al., ICLR’20]
    
40. 40/42 Entity Linking future directions(2) • Improving Entity representations. Yes Require “entity-span” annotations? Use relations? Yes No Use relation? JointEnt [Yamada et al., ACL ’17] KnowBert [Peters, et al, EMNLP ’19] (Indirectly annotated data used) KEPLER [Wang et al., ‘Nov 19] No Yes No DEER [Gillick et al., CoNLL ’19] ERNIE [Zhang et al., ACL ’19] BertEnt [Yamada et al., ’19] EntEval [Chen et al., EMNLP’19] WKLM [Xiong et al., ICLR’20]
    • Various evaluation metrics exist. Entity Typing, Entity disambiguation, Fact completion, QA, …
41. 41/42 Entity Linking future directions(3) • No needs for entity descriptions? [Chen et al., EMNLP’19] [Chen et al., EMNLP’19] introduced 8 entity-evaluation tasks. • Rare : Rare entity prediction(Cloze task) in documents. • CoNLL : Named entity disambiguation. • ERT : Relation typing between two entities. …
42. 42/42 Entity Linking future directions(3) • No needs for entity descriptions?[Chen et al., EMNLP’19] • Rare : Rare entity prediction(Cloze task) in documents. • CoNLL : Named entity disambiguation. • ERT : Relation typing between two entities.
43. 43/42 Entity Linking future directions(3) • No needs for entity descriptions?[Chen et al., EMNLP’19] • Rare : Rare entity prediction(Cloze task) in documents. • CoNLL : Named entity disambiguation. • ERT : Relation typing between two entities.
44. 44/42 Supplementation 44
45. 45/42 EntEval 8 tasks [Chen et al., EMNLP’19] • Rare : Rare entity prediction(Cloze task) in documents. • CoNLL : Named entity disambiguation. • ERT : Relation typing between two entities. • ET : Entity Typing • ESR : Entity Similarity and Relatedness • CAP : Coreference Arc Prediction • EFP : Entity Factuality Prediction • CERP : Contextualized Entity Relationship Prediction
46. 46/42 [Logeswaran et al., ACL’19]’s 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
47. 47/42 Pre-assumption ① : Entity dictionary • They first presupposes only entity dictionary. : its descriptions : entity
48. 48/42 Pre-assumption ② : Worlds( W ) • Each world W has its own : its descriptions : entity
    
    : documents belonging to W
    : labeled spans in , annotated by
49. 49/42 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
50. 50/42 Zero-shot EL datasets [Logeswaran et al., ACL’19] • Each world W constructed from W‘s Wikia.
51. 51/42 Pre-assumption ② : Worlds( W ) = Worlds( W ) : its description : : entity : mention (documents) : constructed from collections meninblack.fandom.com/wiki/Frank_the_Pug
52. 52/42 Pre-assumption ② : Worlds( W ) = Worlds( W ) : its description : : entity : mention (documents) : constructed from collections meninblack.fandom.com/wiki/Frank_the_Pug constructed from
53. 53/42 Pre-assumption ② : Worlds( W ) : its descriptions : entity
    
    : documents belonging to W
    : labeled spans in , annotated by … …
54. 54/42 Pre-assumption ② : Worlds( W ) : its descriptions : entity
    
    : documents belonging to W
    : labeled spans in , annotated by … … This is for “Entity Linking”
55. 55/42 … … 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.
56. 56/42 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
57. 57/42 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
58. 58/42 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
59. 59/42 When and Why DAP?
60. 60/42 How to prepare src/tgt corpus for fine-tuning LM?
61. 61/42 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
62. 62/42 (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
63. 63/42 (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
64. 64/42 (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
65. 65/42 (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
66. 66/42 (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
67. 67/42 (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
68. 68/42 Result for changing resource for pretraining LM • NOTE: this is not DAP.
69. 69/42 (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.
70. 70/42 (B): Is mention - entity description attention powerful? Mention entity cross-attention is effective.
71. 71/42 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