scholarly journals LT-LM: A Novel Non-Autoregressive Language Model for Single-Shot Lattice Rescoring

Author(s):  
Anton Mitrofanov ◽  
Mariya Korenevskaya ◽  
Ivan Podluzhny ◽  
Yuri Khokhlov ◽  
Aleksandr Laptev ◽  
...  
2020 ◽  
Author(s):  
Da-Rong Liu ◽  
Chunxi Liu ◽  
Frank Zhang ◽  
Gabriel Synnaeve ◽  
Yatharth Saraf ◽  
...  

2012 ◽  
Vol 98 (1) ◽  
pp. 5-24 ◽  
Author(s):  
Juan Pino ◽  
Aurelien Waite ◽  
William Byrne

Simple and Efficient Model Filtering in Statistical Machine Translation Data availability and distributed computing techniques have allowed statistical machine translation (SMT) researchers to build larger models. However, decoders need to be able to retrieve information efficiently from these models to be able to translate an input sentence or a set of input sentences. We introduce an easy to implement and general purpose solution to tackle this problem: we store SMT models as a set of key-value pairs in an HFile. We apply this strategy to two specific tasks: test set hierarchical phrase-based rule filtering and n-gram count filtering for language model lattice rescoring. We compare our approach to alternative strategies and show that its trade offs in terms of speed, memory and simplicity are competitive.


Author(s):  
Larisa V. Kalashnikova

The article enlightens the probem of nonsense and its role in the development of creative thinking and fantasy, and the way how the interpretation of nonsense affects children imagination. The function of imagination inherent to a person, and especially to a child, has a powerful potential – to create artificially new metaphorical models, absurd and most incredible situations based on self-amazement. Children are able to measure the properties of unfamiliar objects with the properties of known things. It is not difficult for small researchers to replace incomprehensible meanings with familiar ones; to think over situations, to make analogies, to transfer signs and properties of one object to another. The problem of nonsense research is interesting and relevant. The element of the game is an integral component of nonsense. In the process of playing, children cognize the world, learn to interact with the world, imitating the adults behavior. Imagination and fantasy help the child to invent his own rules of the game, to choose language elements that best suit his ideas. The child uses the learned productive models of the language system to create their own models and their own language, attracting language signs: words, morphs, sentences. Children’s dictionary stimulates word formation and language nomination processes. Nonsense-words are the result of children’s dictionary, speech errors and occazional formations, presented in the form of contamination, phonetic transformations, lexical substitution, implemented on certain models. The first two models are phonetic imitation and hybrid speech, based on the natural language model. The third model of designing nonsense is represented by words that have no meaning at all and can be attributed to words-portmonaie. Due to the flexibility of interframe relationships and the lack of algorithmic thinking, children can not only capture the implicit similarity of objects and phenomena, but also create it through their imagination. Interpretation of nonsense is an effective method of developing imagination in children, because metaphors, nonsense as a means of creating new meanings, modeling new content from fragments of one’s own experience, are a powerful incentive for creative thinking.


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