English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Globally normalized neural sequence models are considered superior to theirlocally normalized equivalents because they may ameliorate the effects of labelbias. However, when considering high-capacity neural parametrizations thatcondition on the whole input sequence, both model classes are theoreticallyequivalent in terms of the distributions they are capable of representing.Thus, the practical advantage of global normalization in the context of modernneural methods remains unclear. In this paper, we attempt to shed light on thisproblem through an empirical study. We extend an approach for search-awaretraining via a continuous relaxation of beam search (Goyal et al., 2017b) inorder to enable training of globally normalized recurrent sequence modelsthrough simple backpropagation. We then use this technique to conduct anempirical study of the interaction between global normalization, high-capacityencoders, and search-aware optimization. We observe that in the context ofinexact search, globally normalized neural models are still more effective thantheir locally normalized counterparts. Further, since our training approach issensitive to warm-starting with pre-trained models, we also propose a novelinitialization strategy based on self-normalization for pre-training globallynormalized models. We perform analysis of our approach on two tasks: CCGsupertagging and Machine Translation, and demonstrate the importance of globalnormalization under different conditions while using search-aware training.

An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search