Detail publikace

Learning Document Embeddings Along With Their Uncertainties

KESIRAJU, S. PLCHOT, O. BURGET, L. GANGASHETTY, S.

Originální název

Learning Document Embeddings Along With Their Uncertainties

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Majority of the text modeling 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 unseen test 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 showthat the proposed systems are robust to over-fitting on unseen test data. The topic ID results show that the proposedmodel outperforms state-of-the-art unsupervised topic models and achieve comparable results to the state-of-the-art fully supervised discriminative models.

Klíčová slova

Bayesian methods, embeddings, topic identification.

Autoři

KESIRAJU, S.; PLCHOT, O.; BURGET, L.; GANGASHETTY, S.

Vydáno

27. 7. 2020

ISSN

2329-9290

Periodikum

IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING

Ročník

2020

Číslo

28

Stát

Spojené státy americké

Strany od

2319

Strany do

2332

Strany počet

14

URL

https://ieeexplore.ieee.org/document/9149686

BibTex

@article{BUT168164,
  author="Santosh {Kesiraju} and Oldřich {Plchot} and Lukáš {Burget} and Suryakanth V {Gangashetty}",
  title="Learning Document Embeddings Along With Their Uncertainties",
  journal="IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING",
  year="2020",
  volume="2020",
  number="28",
  pages="2319--2332",
  doi="10.1109/TASLP.2020.3012062",
  issn="2329-9290",
  url="https://ieeexplore.ieee.org/document/9149686"
}

Dokumenty

kesiraju_acm_transactions on ASLP_28_2020_09149686.pdf