i-Vectors in Language Modeling: An Efficient Way of Domain Adaptation for Feed-Forward Models

Beneš, Karel; Kesiraju, Santosh; Burget, Lukáš

doi:10.21437/interspeech.2018-1070

Cited by 4 publications

(1 citation statement)

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“…L EARNING word and document embeddings have proven to be useful in wide range of information retrieval, speech and natural language processing applications [1]- [5]. These embeddings elicit the latent semantic relations present among the co-occurring words in a sentence or bag-of-words from a document.…”

Section: Introductionmentioning

confidence: 99%

Learning document embeddings along with their uncertainties

Kesiraju,

Plchot,

Burget

et al. 2019

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Majority of the text modelling techniques yield only point-estimates of document embeddings and lack in capturing the uncertainty of the estimates. These uncertainties give a notion of how well the embeddings represent a document. We present Bayesian subspace multinomial model (Bayesian SMM), a generative log-linear model that learns to represent documents in the form of Gaussian distributions, thereby encoding the uncertainty in its covariance. Additionally, in the proposed Bayesian SMM, we address a commonly encountered problem of intractability that appears during variational inference in mixed-logit models. We also present a generative Gaussian linear classifier for topic identification that exploits the uncertainty in document embeddings. Our intrinsic evaluation using perplexity measure shows that the proposed Bayesian SMM fits the data better as compared to the state-of-the-art neural variational document model on (Fisher) speech and (20Newsgroups) text corpora. Our topic identification experiments show that the proposed systems are robust to over-fitting on unseen test data. The topic ID results show that the proposed model is outperforms state-of-the-art unsupervised topic models and achieve comparable results to the state-of-the-art fully supervised discriminative models.

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Section: Introductionmentioning

confidence: 99%