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Utilizing Embeddings 
 In Learning To Rank 
 For Search

Utilizing Embeddings 
 In Learning To Rank 
 For Search

BY Shawn Tsai @LINE TECHPULSE 2019 https://techpulse.line.me/

2102a6b8760bd6f57f672805723dd83a?s=128

line_developers_tw

December 04, 2019
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  1. None
  2. Utilizing Embeddings 
 In Learning To Rank 
 For Search

    > Shawn TSAI / LINE Taiwan Data Dev
  3. Agenda > Search Everywhere > Search Result Relevance > Embeddings

    > Learning To Rank > Search Workflow
  4. Search Everywhere Life on LINE

  5. Search Result Relevance > The main goal is to reduce

    the semantic gap between user query and documents. > The key points: semantic features and ranking function. > Search is a ranking problem. The ordering is more important than the predicted probability of a single instance.
  6. > 聊天記錄⼀一直在資料壓縮中 > 不管怎麼按備份聊天紀錄都不能備份 Limitation: Different description Limitation: No shared

    keywords > 為什什麼有時候賴都不會通知 > 訊息都跑不出來來是怎樣 Search Scoring & Limitation > , > ( ) = () = 1 + + 1 + 1 _ = ∗ Standard similarity function: TF-IDF
  7. Embeddings

  8. Word Embedding > Vector representation > Capturing context of a

    word in a document, semantic/syntactic similarity, relation with other words Source: Efficient Estimation of Word Representations in Vector Space
  9. BERT BERT is a new method of pre-training language representations

    which obtains state-of-the-art results on a wide array of NLP tasks. Source: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Bidirectional Encoder Representations from Transformers
  10. Querying By Vector Representation Sent. Encoding
 By Pre-trained BERT
 Model

    Query Document Vecs Index Query Vec Documents Document Vecs Online Offline Nearest Neighbor Search Build 
 NN 
 Index
  11. Learning To Rank

  12. Learning To Rank > Applying machine learning to construct ranking

    models for information retrieval systems > Caring more about ranking rather than rating prediction > Scoring by machine learning • Creating document index by Elasticsearch • Using embeddings to train ranking models • Serving search queries by Elasticsearch with ranking models
  13. Filters Search Architecture Documents Query Filter Index ES + Re-ranking

    BERT Matches Ranked Results NER … Scoring Index Ranking Models
  14. Ƃ Custom Scoring Function Ƃ

  15. Search Workflow With Learning To Rank User’s Needs Measure Relevance

    Pre-process Inverted-index Features Selection Ranking Models Scoring Function NDCG MAP Precision@k Deploy Monitoring Feedback Evaluation Build Index Learning To Rank Serve Data
  16. More Consideration > Good judge lists matching user needs of

    search quality > Good metrics measuring search results > Incorporating with embeddings into scoring function > Synchronizing the version between indexing and serving layers > A/B testing
  17. Thank you