There is growing interest in software migration as the development of software and society. Manually migrating projects between languages is error-prone and expensive. In recent years, researchers have begun to explor...
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ISBN:
(纸本)9781665457019
There is growing interest in software migration as the development of software and society. Manually migrating projects between languages is error-prone and expensive. In recent years, researchers have begun to explore automatic program translation using supervised deep learning techniques by learning from large-scale parallel code corpus. However, parallel resources are scarce in the programming language domain, and it is costly to collect bilingual data manually. To address this issue, several unsupervised programming translation systems are proposed. However, these systems still rely on huge monolingual source code to train, which is very expensive. Besides, these models cannot perform well for translating the languages that are not seen during the pre-training procedure. In this paper, we propose SDA-Trans, a syntax and domain-aware model for program translation, which leverages the syntax structure and domain knowledge to enhance the cross-lingual transfer ability. SDA-Trans adopts unsupervised training on a smaller-scale corpus, including Python and Java monolingual programs. The experimental results on function translation tasks between Python, Java, and C++ show that SDA-Trans outperforms many large-scale pre-trained models, especially for unseen language translation.
The differences of English and Chinese in syntax structure are analyzed and the influence of these differences on the Chinese English learners and the method to solve the negative impact is explored.
The differences of English and Chinese in syntax structure are analyzed and the influence of these differences on the Chinese English learners and the method to solve the negative impact is explored.
Attention mechanism has been proved to be able to improve the quality of neural machine translation by selectively focusing on partial words of a source sentence during translation process. Attention mechanism usually...
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ISBN:
(数字)9789811517211
ISBN:
(纸本)9789811517211;9789811517204
Attention mechanism has been proved to be able to improve the quality of neural machine translation by selectively focusing on partial words of a source sentence during translation process. Attention mechanism usually focuses on local attention by using solely the linear index distance of words while ignores syntax structures of sentences. In this paper, we extend local attention through syntax distance constraint, and propose an attention mechanism based on a new syntactic branch distance, which simultaneously pays attention to words with similar linear index distances and syntax-related words. Based on the English-to-German translation task, experiment results showed that our model outperforms a recent baseline method with an improvement of 1.61 BLEU points, demonstrating the effectiveness of the proposed model.
In this paper, we propose a sequential neural encoder with latent structured description (SNELSD) for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural en...
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In this paper, we propose a sequential neural encoder with latent structured description (SNELSD) for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural encoders, i.e., recurrent neural networks with long short-term memory (LSTM) units, to consider the compositionality of languages in semantic modeling. An SNELSD model has a hierarchical structure that includes a detection layer and a description layer. The detection layer predicts the boundaries of latent word chunks in an input sentence and derives a chunk-level vector for each word. The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs. These output vectors are further concatenated with word vectors or the outputs of a chain LSTM encoder to obtain the final sentence representation. All the model parameters are learned in an end-to-end manner without a dependency on additional text chunking or syntax parsing. A natural language inference task and a sentiment analysis task are adopted to evaluate the performance of our proposed model. The experimental results demonstrate the effectiveness of the proposed SNELSD model on exploring task-dependent chunking patterns during the semantic modeling of sentences. Furthermore, the proposed method achieves better performance than conventional chain LSTMs and tree-structured LSTMs on both tasks.
This paper proposes an idea to integrate Japanese case frame into chunk-based dependency-to-string model. At first, case frames are acquired from Japanese chunk-based dependency analysis results. Then case frames are ...
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ISBN:
(纸本)9783662457016;9783662457009
This paper proposes an idea to integrate Japanese case frame into chunk-based dependency-to-string model. At first, case frames are acquired from Japanese chunk-based dependency analysis results. Then case frames are used to constraint rule extraction and decoding in chunk-based dependency-to-string model. Experimental results show that the proposed method performs well on long structural reordering and lexical translation, and achieves better performance than hierarchical phrase-based model and word-based dependency-to-string model on Japanese to Chinese test sets.
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