WO2014190220A3 - Language model trained using predicted queries from statistical machine translation - Google Patents

Language model trained using predicted queries from statistical machine translation Download PDF

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Publication number
WO2014190220A3
WO2014190220A3 PCT/US2014/039258 US2014039258W WO2014190220A3 WO 2014190220 A3 WO2014190220 A3 WO 2014190220A3 US 2014039258 W US2014039258 W US 2014039258W WO 2014190220 A3 WO2014190220 A3 WO 2014190220A3
Authority
WO
WIPO (PCT)
Prior art keywords
language model
smt
model
content
predicted queries
Prior art date
Application number
PCT/US2014/039258
Other languages
French (fr)
Other versions
WO2014190220A2 (en
Inventor
Michael Levit
Dilek Hakkani-Tur
Gokhan Tur
Original Assignee
Microsoft Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Microsoft Corporation filed Critical Microsoft Corporation
Priority to EP14733810.7A priority Critical patent/EP2941719A2/en
Publication of WO2014190220A2 publication Critical patent/WO2014190220A2/en
Publication of WO2014190220A3 publication Critical patent/WO2014190220A3/en

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Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/063—Training
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/9032—Query formulation
    • G06F16/90332—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/40—Processing or translation of natural language
    • G06F40/42—Data-driven translation
    • G06F40/44—Statistical methods, e.g. probability models
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/183—Speech classification or search using natural language modelling using context dependencies, e.g. language models
    • G10L15/19—Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules
    • G10L15/197—Probabilistic grammars, e.g. word n-grams

Abstract

A Statistical Machine Translation (SMT) model (165) is trained using pairs of sentences that include content obtained from one or more content sources (e.g. feed(s)) with corresponding queries that have been used to access the content. A query click graph (130) may be used to assist in determining candidate pairs for the SMT training data. All/portion of the candidate pairs may be used to train the SMT model. After training the SMT model using the SMT training data, the SMT model is applied to content to determine predicted queries (154) that may be used to search for the content. The predicted queries are used to train a language model, such as a query language model. The query language model may be interpolated other language models, such as a background language model, as well as a feed language model trained using the content used in determining the predicted queries.
PCT/US2014/039258 2013-05-24 2014-05-23 Language model trained using predicted queries from statistical machine translation WO2014190220A2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP14733810.7A EP2941719A2 (en) 2013-05-24 2014-05-23 Language model trained using predicted queries from statistical machine translation

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US13/902,470 2013-05-24
US13/902,470 US20140350931A1 (en) 2013-05-24 2013-05-24 Language model trained using predicted queries from statistical machine translation

Publications (2)

Publication Number Publication Date
WO2014190220A2 WO2014190220A2 (en) 2014-11-27
WO2014190220A3 true WO2014190220A3 (en) 2015-05-14

Family

ID=51023074

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2014/039258 WO2014190220A2 (en) 2013-05-24 2014-05-23 Language model trained using predicted queries from statistical machine translation

Country Status (3)

Country Link
US (1) US20140350931A1 (en)
EP (1) EP2941719A2 (en)
WO (1) WO2014190220A2 (en)

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US11003838B2 (en) 2011-04-18 2021-05-11 Sdl Inc. Systems and methods for monitoring post translation editing
US10261994B2 (en) 2012-05-25 2019-04-16 Sdl Inc. Method and system for automatic management of reputation of translators
US9213694B2 (en) * 2013-10-10 2015-12-15 Language Weaver, Inc. Efficient online domain adaptation
US10452786B2 (en) * 2014-12-29 2019-10-22 Paypal, Inc. Use of statistical flow data for machine translations between different languages
KR102325724B1 (en) 2015-02-28 2021-11-15 삼성전자주식회사 Synchronization of Text Data among a plurality of Devices
CN111971679A (en) * 2018-01-26 2020-11-20 威盖特技术美国有限合伙人公司 Generating natural language recommendations based on an industry language model

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Also Published As

Publication number Publication date
WO2014190220A2 (en) 2014-11-27
EP2941719A2 (en) 2015-11-11
US20140350931A1 (en) 2014-11-27

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