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How can we compare unstructured, structured and self-structured knowledge representation?

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Unstructured and Structured KBs
Workshop at AKBC 2020
June 25, 2020
https://uskb-workshop.github.io/

Abstract:
Several approaches have been proposed to represent world knowledge. It can be unstructured in text corpora, organised in structured collections (e.g, KBs, key-value memories), or self-structured in the parameters of a neural model. However, it is still unclear how to compare these different solutions. Most of the existing NLP benchmarks focus on tasks that humans can solve by just examining local information. In this talk I will review some knowledge-intensive tasks, that require to seek knowledge in a large body of documents even for humans in order to be solved. I will present some of the latest models proposed to solve those and which representation they use for knowledge. Moreover, I will present some ideas to investigate models' explainability in this setting.

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How can we compare unstructured, structured and self-structured knowledge representation?

  1. 1. How can we compare unstructured, structured and self-structured knowledge representation? Fabio Petroni 25 June 2020 1 Unstructured and Structured KBs @
  2. 2. Structured large-scare textual corpora Knowledge Representation knowledge graph key-value memory Unstructured soft hard
  3. 3. Structured machinelarge-scare textual corpora Knowledge Representation knowledge graph key-value memory Unstructured Self-Structured soft hard
  4. 4. Structured machinelarge-scare textual corpora Knowledge Representation knowledge graph key-value memory Unstructured Self-Structured How can we compare these approaches (+combinations)? soft hard
  5. 5. Structured machinelarge-scare textual corpora Knowledge Representation knowledge graph key-value memory Unstructured Self-Structured downstream tasks soft hard
  6. 6. 6Current NLP benchmarks e.g., natural language inference s1: At the other end of Pennsylvania Avenue, people began to line up for a White House tour. s2: People formed a line at the end of Pennsylvania Avenue. entailment - focus on reading comprehension - emergence of general architectures (e.g., BERT) - local information is sufficient to solve the task ...
  7. 7. Knowledge Intensive NLP tasks - require to seek knowledge in a large body of documents even for humans to be solved Knowledge Source Unstructured
  8. 8. Knowledge Intensive NLP tasks - require to seek knowledge in a large body of documents even for humans to be solved Structured Knowledge Source Unstructured+
  9. 9. 9Knowledge Intensive NLP tasks 1 - Slot Filling 2 - Entity Linking 3 - Open Domain QA 4 - Fact Checking 5 - Factual Generations
  10. 10. Knowledge Intensive NLP task 1 - Slot Filling GiacomoTedesco date of birth place of birth occupation position played on team / speciality TAC-KBP challenges McNamee and Dang, 2009; Ji et al., 2010; Surdeanu, 2013; Surdeanu and Ji, 2014 collect information on certain relations (or slots) of entities from large collections of natural language text
  11. 11. Knowledge Intensive NLP task 1 - Slot Filling GiacomoTedesco plays in _____ position . <GiacomoTedesco, position played on team> What position does GiacomoTedesco play? structured query natural question cloze-style question several ways to approach the problem GiacomoTedesco date of birth place of birth occupation position played on team / speciality
  12. 12. 12 Petroni et al, 2019-2020 single token answers T-REx (Elsahar et al, 2018) Google-RE https://code.google.com/archive/ p/relation-extraction-corpus/ https://github.com/facebookresearch/LAMA Slot Filling
  13. 13. 131 0 25 50 75 100 RE RE-ora Wikidata automatic KG structured human curated structured accuracy Sorokin and Gurevych (2017) Petroni et al, 2019-2020 structured query structured query
  14. 14. 141 0 25 50 75 100 RE RE-ora BERT Wikidata automatic KG structured self- structured unstructured solution = read all Wikipedia to predict accuracy human curated structured Petroni et al, 2019-2020 structured query structured query cloze-style question
  15. 15. 151 0 25 50 75 100 RE RE-ora BERT DrQA BERT-ret BERT-ora Wikidata automatic KG structured self- structured indexed human curated structured unstructured solution = read all Wikipedia to predict accuracy Petroni et al, 2019-2020 structured query structured query cloze-style question enriched cloze-style question natural question
  16. 16. 161 0 25 50 75 100 RE RE-ora BERT DrQA BERT-ret BERT-ora Wikidata automatic KG structured self- structured indexed human curated structured unstructured solution = read all Wikipedia to predict accuracy soft hard hardcoded rules for what knowledge is KB in- parameters Petroni et al, 2019-2020 structured query structured query cloze-style question cloze-style question natural question
  17. 17. 17LAMA limitations - Single token might favour BERT - Wikidata gets all answer right - Explainability not assessed GiacomoTedesco plays in _____ position . midfielder provenance answer both open/close book can get the answer for the wrong reason evidence from the knowledge source
  18. 18. Knowledge Intensive NLP task 2 - Entity Linking 18 The most comprehensive photographic handbook for mushroom is authored by Michael Jordan. wiki/Michael_Jordan_(mycologist) is the task of assigning a unique identity to entities mentioned in text
  19. 19. 19 dense retrieval with MIPS and bi-encoder wikipedia dense space […] authored by [SE] Michael Jordan [EE]. is an English mycologist Michael Jordan (m) is former basketball player Michael Jordan T T Tiger T Ferrari Domus Aurea Carriage Moon Jaguar … BI-ENCODER MIPS Dog Lake Avernus Mannheim Diego Maradona 5.9M points
  20. 20. TAC-KBP 2010 20 He et al. (2013) 81.0 Sun et al. (2015) 83.9 Yamada et al. (2016) 85.5 Globerson et al. (2016) 87.2 Sil et al. (2018) 87.4 Nie et al. (2018) 89.1 Raiman and Raiman (2018) 90.9 Cao et al. (2018) 91.0 Gillick et al. (2019) 87.0 Wu et al. (2019) 94.5 Févry et al (2020) 94.9 dense retrieval bi-encoder Ji et al. 2010
  21. 21. TAC-KBP 2010 21Ji et al. 2010 He et al. (2013) 81.0 Sun et al. (2015) 83.9 Yamada et al. (2016) 85.5 Globerson et al. (2016) 87.2 Sil et al. (2018) 87.4 Nie et al. (2018) 89.1 Raiman and Raiman (2018) 90.9 Cao et al. (2018) 91.0 Gillick et al. (2019) 87.0 Wu et al. (2019) 94.5 Févry et al (2020) 94.9 uniform candidate set - whole Wikipedia dense retrieval bi-encoder TAC-KBP ks ~700K entities real-world scenario each dataset defines a different set of candidate entities
  22. 22. TAC-KBP 2010 22 He et al. (2013) 81.0 Sun et al. (2015) 83.9 Yamada et al. (2016) 85.5 Globerson et al. (2016) 87.2 Sil et al. (2018) 87.4 Nie et al. (2018) 89.1 Raiman and Raiman (2018) 90.9 Cao et al. (2018) 91.0 Gillick et al. (2019) 87.0 Wu et al. (2019) 94.5 Févry et al (2020) 94.9 Févry et al (2020) 91.4 Wu et al. (2019) 92.8all Wikipedia ~5.9M entities TAC-KBP ks ~700K entities Ji et al. 2010 real-world scenario
  23. 23. Knowledge Intensive NLP task 3 - Open Domain QA What's the highest mountain in Europe? - TriviaQA (Joshi et al., 2017) - HotpotQA (Yang et al., 2018) - Natural Questions (Kwiatkowski et al., 2019) - ELI5 (Fan et al., 2019) - ...
  24. 24. 24 dense retrieval with MIPS and bi-encoder wikipedia dense space What’s the highest mountain in Europe? is the second- highest mountain Mont Blanc p1 Mount Elbrus is a dormant vulcano Mount Elbrus p1 T T Tiger p5 T Ferrari p1 Domus Aurea p5 Carriage p4 Moon p2 Jaguar p4 … BI-ENCODER MIPS Dog p6 Lake Avernus p2 Mannheim p1 Diego Maradona p1 21M points
  25. 25. Exact Match 25 Natural Questions open dev TriviaQA official test Roberts et al. 2020 T5 36.6 60.5 Guu et al. 2020 REALM 40.4 Karpukhin et al. 2020 DPR 41.5 Lewis et al. 2020 RAG 44.5 68
  26. 26. Exact Match 26 Natural Questions TriviaQA Roberts et al. 2020 T5 36.6 60.5 Guu et al. 2020 REALM 40.4 Karpukhin et al. 2020 DPR 41.5 Lewis et al. 2020 RAG 44.5 68 Self-Structured Unstructured + Structured + Self-Structured
  27. 27. Limitations 27 Credit: Firstname Lastname - different split of the data - explainability not assessed provenance answer both open/close book can get the answer for the wrong reason evidence from the knowledge source text, lists, tables, images What's the highest mountain in Europe? Mount Elbrus
  28. 28. Knowledge Intensive NLP task 4 - Fact Checking 28 FEVER Thorne et al., 2018-2019 Lorelai Gilmore's father is named Robert. claim SUPPORTS REFUTES NOT ENOUGH INFO answer provenance 3-way classification
  29. 29. Label Accuracy 29 3-way 2-way Zhong et al. 2020 DREAM 76.8 - Thorne et al. 2020 RoBERTa - 92.2* Lewis et al. 2019 BART 64.0 81.1 Lewis et al. 2020 RAG 72.5 89.5 * with oracle evidence
  30. 30. Discussion 30 FEVER score - elegant way to combine explainability and downstream performance only award points for accuracy if the correct evidence is found a ton of manual annotations FEVER is an artificial task fact-checking in the real word is another game
  31. 31. Knowledge Intensive NLP task 5 - Factual Generations 31 GPT2 generation Massarelli et al. (2019) Princess Margaret, Countess of Snowdon, (Margaret Rose 21 August 1930 - 9 February 2002) was the younger daughter of King GeorgeVI and Queen ElizabethThe Queen Mother and the only sibling of Queen Elizabeth II. She married Antony Armstrong-Jones, a photographer, in 1960. It was the first marriage for the Queen and the first for Prince Philip, Duke of Edinburgh. After divorcing Armstrong-Jones in 1978, she married Group Captain PeterTownsend in June that same year. She died at the age of 71 on 9 February 2002. Why did Princess Margaret marry Antony Armstrong-Jones? prompt Delayed Beam Search up to 64% of generated sentences with claims are SUPPORTED
  32. 32. Jeopardy Question Generation 32 Input: The Divine Comedy BART: This epic poem by Dante is divided into three parts: the Inferno,The Purgatorio & the Purgatorio RAG: This 14th Century work is divided into 3 sections:“inferno”, “Purgatorio” & “Paradiso” Factuality Specificity BART Better 7.1% 16.8% RAG better 42.7% 37.4% both good 11.7% 11.8% both poor 17.7% 6.9% no majority 20.8% 20.1% human evaluation Lewis et al. (2020)
  33. 33. 33 Fabio Petroni 1Jeopardy Question Generation Input: Hemingway RAG: “The Sun Also Rises” is a novel by this author of "A Farewell to Arms" Document 1: his works are considered classics of American literature ... His wartime experiences formed the basis for his novel ”A Farewell to Arms” (1929) ... Document 2: ... artists of the 1920s ”Lost Generation” expatriate community. His debut novel, ”The Sun Also Rises”, was published in 1926. BO S ” The Sun Also R ises ” is a novel by thisauthor of ” A Fare w ell to Arm s ” Doc 1 Doc 2 Doc 3 Doc 4 Doc 5 33 Lewis et al. (2020)
  34. 34. Interaction Between Parametric / Non-Parametric Knowledge Retrieved documents cue correct responses from BART: Feed BART with input Hemingway and partial decoding “The Sun: Completion: “The Sun also Rises” is a novel by this author of “the Sun Also Rises” Feed BART with input Hemingway and partial decoding “The Sun Also Rises” is a novel by this author of “A: Completion: “The Sun also Rises” is a novel by this author of “A Farewell to Arms” Lewis et al. (2020)
  35. 35. Conclusion 35 Can a model read the web and autonomously write an encyclopedia ? Encoding unit of text with a LM seems a really promising way to build knowledge bases We should use a variegated set of knowledge intensive language tasks to evaluate knowledge representation The ultimate Knowledge Intensive task
  36. 36. THANK YOU 36 @Fabio_Petroni

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