Stephan Mandt


2019

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Autoregressive Text Generation Beyond Feedback Loops
Florian Schmidt | Stephan Mandt | Thomas Hofmann
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Autoregressive state transitions, where predictions are conditioned on past predictions, are the predominant choice for both deterministic and stochastic sequential models. However, autoregressive feedback exposes the evolution of the hidden state trajectory to potential biases from well-known train-test discrepancies. In this paper, we combine a latent state space model with a CRF observation model. We argue that such autoregressive observation models form an interesting middle ground that expresses local correlations on the word level but keeps the state evolution non-autoregressive. On unconditional sentence generation we show performance improvements compared to RNN and GAN baselines while avoiding some prototypical failure modes of autoregressive models.

2018

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Continuous Word Embedding Fusion via Spectral Decomposition
Tianfan Fu | Cheng Zhang | Stephan Mandt
Proceedings of the 22nd Conference on Computational Natural Language Learning

Word embeddings have become a mainstream tool in statistical natural language processing. Practitioners often use pre-trained word vectors, which were trained on large generic text corpora, and which are readily available on the web. However, pre-trained word vectors oftentimes lack important words from specific domains. It is therefore often desirable to extend the vocabulary and embed new words into a set of pre-trained word vectors. In this paper, we present an efficient method for including new words from a specialized corpus, containing new words, into pre-trained generic word embeddings. We build on the established view of word embeddings as matrix factorizations to present a spectral algorithm for this task. Experiments on several domain-specific corpora with specialized vocabularies demonstrate that our method is able to embed the new words efficiently into the original embedding space. Compared to competing methods, our method is faster, parameter-free, and deterministic.