MX2018011305A - Técnicas para corregir el desvío de entrenamiento lingüístico en los datos de entrenamiento. - Google Patents

Técnicas para corregir el desvío de entrenamiento lingüístico en los datos de entrenamiento.

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Publication number
MX2018011305A
MX2018011305A MX2018011305A MX2018011305A MX2018011305A MX 2018011305 A MX2018011305 A MX 2018011305A MX 2018011305 A MX2018011305 A MX 2018011305A MX 2018011305 A MX2018011305 A MX 2018011305A MX 2018011305 A MX2018011305 A MX 2018011305A
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Mexico
Prior art keywords
training
model
biases
training data
sentences
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MX2018011305A
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English (en)
Inventor
Shroff Gautam
Vig Lovekesh
Agarwal Puneet
Patidar Mayur
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Tata Consultancy Services Ltd
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Publication of MX2018011305A publication Critical patent/MX2018011305A/es

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/046—Forward inferencing; Production systems

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Mathematical Physics (AREA)
  • Evolutionary Computation (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Probability & Statistics with Applications (AREA)
  • Databases & Information Systems (AREA)
  • Machine Translation (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

En los sistemas de asistente automatizado, se utiliza un modelo de aprendizaje profundo en forma de un clasificador de memoria larga a corto plazo (LSTM) para asignar preguntas a clases, con cada clase teniendo una respuesta depurada manualmente. Un equipo de expertos crea manualmente los datos de entrenamiento utilizados para entrenar a este clasificador. Confiar en la depuración humana a menudo hace que dichos desvíos de entrenamiento lingüístico se arraiguen en los datos de entrenamiento, ya que cada individuo tiene un estilo específico de escribir el lenguaje natural y usa algunas palabras en un contexto específico solamente. Los modelos profundos terminan aprendiendo estos desvíos, en lugar de las palabras conceptuales centrales de las clases objetivo. Para corregir estos desvíos, las oraciones significativas se generan automáticamente usando un modelo generativo, y luego se usan para entrenar un modelo de clasificación. Por ejemplo, se utiliza un auto-codificador variacional (VAE) como modelo generativo para generar oraciones novedosas y se utiliza un modelo de lenguaje (LM) para seleccionar oraciones basadas en la probabilidad.
MX2018011305A 2017-09-18 2018-09-17 Técnicas para corregir el desvío de entrenamiento lingüístico en los datos de entrenamiento. MX2018011305A (es)

Applications Claiming Priority (1)

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IN201721033035 2017-09-18

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MX2018011305A true MX2018011305A (es) 2019-07-04

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MX2018011305A MX2018011305A (es) 2017-09-18 2018-09-17 Técnicas para corregir el desvío de entrenamiento lingüístico en los datos de entrenamiento.

Country Status (6)

Country Link
US (1) US11373090B2 (es)
JP (1) JP6606243B2 (es)
AU (1) AU2018232914B2 (es)
BR (1) BR102018068925A2 (es)
CA (1) CA3017655C (es)
MX (1) MX2018011305A (es)

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CN110580289B (zh) * 2019-08-28 2021-10-29 浙江工业大学 一种基于堆叠自动编码器和引文网络的科技论文分类方法
CN110795913B (zh) * 2019-09-30 2024-04-12 北京大米科技有限公司 一种文本编码方法、装置、存储介质及终端
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Also Published As

Publication number Publication date
CA3017655A1 (en) 2019-03-18
AU2018232914A1 (en) 2019-04-04
BR102018068925A2 (pt) 2019-05-28
US20190087728A1 (en) 2019-03-21
AU2018232914B2 (en) 2020-07-02
JP2019057280A (ja) 2019-04-11
US11373090B2 (en) 2022-06-28
JP6606243B2 (ja) 2019-11-13
CA3017655C (en) 2021-04-20

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