Combining Lexical Semantic Resources with Question & Answer Archives for Translation-Based Answer Finding

Delphine Bernhard and Iryna GurevvchUbiquitous Knowledge Processing (UKP) Lab

Computer Science DepartmentTechnische Universit¨at Darmstadt, Hochschulstraße 10

D-64289 Darmstadt, Germany

ACL 2009

Reporter: Kan-Wen TienDate: 2009.10.22

Outlines

• Introduction• Related Work• Parallel Datasets• Semantic Relatedness Experiments• Answer Finding Experiments• Conclusion

• Introduction• Related Work• Parallel Datasets• Semantic Relatedness Experiments• Answer Finding Experiments• Conclusion

Introduction

• Lexical gap between queries and documents or questions and answers

• Several solutions：– Query reformulation, query paraphrasing– Query expansion – Semantic information retrieval

Introduction

• Several solutions：– Integrate monolingual statistical translation

models in the retrieval process (1999)• Drawback: limited availability of truly parallel

monolingual corpora

• Training data often consist in question-answer pairs and usually extracted from the evaluation corpus itself

• Introduction

• Related Work• Parallel Datasets• Semantic Relatedness Experiments• Answer Finding Experiments• Conclusion

Related Work

• Statistical translation models for retrieval• Built synthetic training data • Train translation models on Q&A pairs – Answers -> source language– Questions -> target language

• Select the most important terms to build compact translation models

• Introduction• Related Work

• Parallel Datasets• Semantic Relatedness Experiments• Answer Finding Experiments• Conclusion

Parallel Datasets

• Different data resources:(1)Manually-tagged question reformulations

and question-answer pairs from the WikiAnswers social Q&A site

(2) Glosses from WordNet, Wiktionary, Wikipedia and Simple Wikipedia

Parallel Datasets

(1) Manually-tagged question reformulations and question-answer pairs

• From social Q&A sites: WikiAnswers (WA)– Question-Answer Pairs (WAQA)

– Question Reformulations (WAQ)

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Parallel Datasets

(2) Glosses from WordNet, Wiktionary, Wikipedia and Simple Wikipedia

• Lexical Semantic Resources (LSR)– Word sense alignment

• Example !

Parallel Datasets

• Example: “moon”– Wordnet (sense 1): The natural satellite of the

Earth.– English Wiktionary: The Moon, the satellite of

planet Earth.– English Wikipedia: The Moon (Latin: Luna) is

Earth’s only natural satellite and the fifth largest natural satellite in the Solar System.

Parallel Datasets

Three datasets: • Question-Answer Pairs (WAQA)

1,227,362 parallel pairs

• Question Reformulations (WAQ)4,379,620 parallel pairs

• Lexical Semantic Resources (LSR)397,136 pairs

Parallel Datasets

• Translation Model Training– Pre-processing steps

– GIZA++ SMT Toolkit -> word-to-word translation probabilities

– IBM translation model 1

Parallel Datasets

• Combination of the datasets– Lin (combination of models after training)

– Pool (concatenating the corpora before training)

Parallel Datasets

Parallel Datasets

Parallel Datasets

• Introduction• Related Work• Parallel Datasets

• Semantic Relatedness Experiments• Answer Finding Experiments• Conclusion

Semantic Relatedness Experiments• Goal: Word translation probabilities vs.

Concept vector based measure

• Concept vector based measure relying on Explicit Semantic Analysis(Gabrilovich and Markovitch, 2007)

• Compare with traditional semantic relatedness measures

Semantic Relatedness Experiments

Semantic Relatedness Experiments

• Testing data set: 353 word-to-word pairs– Created by Finkelstein et al. (2002)– Fin1-153: 153 pairs– Fin2-200: 200 pairs

Semantic Relatedness Experiments

• Testing data set: 353 word-to-word pairs– Created by Finkelstein et al. (2002)– Fin1-153: 153 pairs– Fin2-200: 200 pairs

Semantic Relatedness Experiments

• Use Spearman’s Rank Correlation Coefficients (-1, 0, +1)

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Semantic Relatedness Experiments

• Use Spearman’s Rank Correlation Coefficients (-1, 0, +1)

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• Introduction• Related Work• Parallel Datasets• Semantic Relatedness Experiments

• Answer Finding Experiments• Conclusion

Answer Finding Experiments

• Goal: provide an extrinsic evaluation of the translation probabilities by employing them in an answer finding task.

• Using a ranking function to perform retrieval

Answer Finding Experiments

• Ranking function (β = 0.8, λ = 0.5)

Answer Finding Experiments

• Ranking function (β = 0.8, λ = 0.5)

Answer Finding Experiments

• Ranking function (β = 0.8, λ = 0.5)

Query likelihood modelTranslation model

Answer Finding Experiments

• Testing data: Microsoft Research QA Corpus• 1,364 questions, 9,780 answers• 5 levels of relevance judgements:

0: No Judgement Made1: Extract Answers3: Off Topic4: On Topic, Off Target5: Partial Answer

Answer Finding Experiments

• Testing data: Microsoft Research QA Corpus• 1,364 questions, 9,780 answers• 5 levels of relevance judgements:

0: No Judgement Made1: Extract Answers3: Off Topic4: On Topic, Off Target5: Partial Answer

Answer Finding Experiments

Answer Finding Experiments

• Mean Average Precision (MAP)• Mean R-Precision (R-prec)• Baselines: – Query likelihood model (QLM) ---> β = 0

– LuceneQuery likelihood model Translation model

Answer Finding Experiments

Answer Finding Experiments

Answer Finding Experiments

Answer Finding Experiments

Answer Finding Experiments

• Introduction• Related Work• Parallel Datasets• Semantic Relatedness Experiments• Answer Finding Experiments

• Conclusion

Conclusion

• Propose new kinds of datasets for training• Provide the first intrinsic evaluation of word

translation probabilities with respect to human relatedness rankings for reference word pairs

• Models based on translation probabilities for answer finding

Thank you !