计算机科学
机器翻译
人工智能
自然语言处理
预处理器
语义学(计算机科学)
人工神经网络
基于迁移的机器翻译
翻译(生物学)
深度学习
程序设计语言
基于实例的机器翻译
生物化学
化学
信使核糖核酸
基因
作者
Oleksii Levkovskyi,Wei Li
标识
DOI:10.1109/southeastcon45413.2021.9401852
摘要
Formal logic expressions are commonly written in standardized mathematical notation. Learning this notation typically requires many years of experience and is not an explicit part of undergraduate academic curricula. Constructing and comprehending logical predicates can feel difficult and unintuitive. We hypothesized that this process can be automated using neural machine translation. Most machine translation techniques involve word-based segmentation as a preprocessing step. Given the nature of our custom dataset, hosts first-order-logic (FOL) semantics primarily in unigram tokens, the word-based approach does not seem applicable. The proposed solution was to automate the translation of short English sentences into FOL expressions using character-level prediction in a recurrent neural network model. We trained four encoder-decoder models (LSTM, Bidirectional GRU with Attention, and two variants of Bi-directional LSTM with Attention). Our experimental results showed that several established neural translation techniques can be implemented to produce highly accurate machine translators of English sentences to FOL formalisms, given only characters as markers of semantics. We also demonstrated that attention-based enhancement to the encoder-decoder architecture can vastly improve translation accuracy. Most machine translation techniques involve word-based segmentation as a preprocessing step. Given the nature of our custom dataset, hosts first-order-logic (FOL) semantics primarily in unigram tokens, the word-based approach does not seem applicable. The proposed solution was to automate the translation of short English sentences into FOL expressions using character-level prediction in a recurrent neural network model. We trained four encoder-decoder models (LSTM, Bidirectional GRU with Attention, and two variants of Bi-directional LSTM with Attention). Our experimental results showed that several established neural translation techniques can be implemented to produce highly accurate machine translators of English sentences to FOL formalisms, given only characters as markers of semantics. We also demonstrated that attention-based enhancement to the encoder-decoder architecture can vastly improve translation accuracy. We trained four encoder-decoder models (LSTM, Bidirectional GRU with Attention, and two variants of Bi-directional LSTM with Attention). Our experimental results showed that several established neural translation techniques can be implemented to produce highly accurate machine translators of English sentences to FOL formalisms, given only characters as markers of semantics. We also demonstrated that attention-based enhancement to the encoder-decoder architecture can vastly improve translation accuracy.
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