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- W4300604996 abstract "Encoder-decoder networks are popular for modeling sequences probabilistically in many applications. These models use the power of the Long Short-Term Memory (LSTM) architecture to capture the full dependence among variables, unlike earlier models like CRFs that typically assumed conditional independence among non-adjacent variables. However in practice encoder-decoder models exhibit a bias towards short sequences that surprisingly gets worse with increasing beam size. In this paper we show that such phenomenon is due to a discrepancy between the full sequence margin and the per-element margin enforced by the locally conditioned training objective of a encoder-decoder model. The discrepancy more adversely impacts long sequences, explaining the bias towards predicting short sequences. For the case where the predicted sequences come from a closed set, we show that a globally conditioned model alleviates the above problems of encoder-decoder models. From a practical point of view, our proposed model also eliminates the need for a beam-search during inference, which reduces to an efficient dot-product based search in a vector-space." @default.
- W4300604996 created "2022-10-03" @default.
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- W4300604996 date "2016-06-10" @default.
- W4300604996 modified "2023-09-26" @default.
- W4300604996 title "Length bias in Encoder Decoder Models and a Case for Global Conditioning" @default.
- W4300604996 doi "https://doi.org/10.48550/arxiv.1606.03402" @default.
- W4300604996 hasPublicationYear "2016" @default.
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