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Compositional neural network language models for agglutinative languages

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info:eu-repo/semantics/closedAccess

Date

2016

Author

Arısoy, Ebru
Saraçlar, Murat

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Citation

Arisoy, E., Saraclar, M., Compositional Neural Network Language Models for Agglutinative Languages. p. 3494-3498.

Abstract

Continuous space language models (CSLMs) have been proven to be successful in speech recognition. With proper training of the word embeddings, words that are semantically or syntactically related are expected to be mapped to nearby locations in the continuous space. In agglutinative languages, words are made up of concatenation of stems and suffixes and, as a result, compositional modeling is important. However, when trained on word tokens, CSLMs do not explicitly consider this structure. In this paper, we explore compositional modeling of stems and suffixes in a long short-term memory neural network language model. Our proposed models jointly learn distributed representations for stems and endings (concatenation of suffixes) and predict the probability for stem and ending sequences. Experiments on the Turkish Broadcast news transcription task show that further gains on top of a state-of-theart stem-ending-based n-gram language model can be obtained with the proposed models.

Source

Conference: 17th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2016) Location: San Francisco, CA Date: SEP 08-12, 2016

URI

http://dx.doi.org/10.21437/Interspeech.2016-1239
https://hdl.handle.net/20.500.11779/686

Collections

  • Elektrik Elektronik Mühendisliği Bölümü koleksiyonu [55]
  • Scopus İndeksli Yayınlar Koleksiyonu [526]
  • WOS İndeksli Yayınlar Koleksiyonu [532]

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