论文标题
孟加拉语的连续空间神经语言模型
A Continuous Space Neural Language Model for Bengali Language
论文作者
论文摘要
通常采用语言模型来估计各种语言单元的概率分布，使其成为自然语言处理的基本部分之一。语言模型的应用包括各种各样的任务，例如文本摘要，翻译和分类。对于像孟加拉这样低资源语言，到目前为止，该领域的研究至少可以被认为是狭窄的，其中一些基于传统的基于计数的模型提出了一些。本文试图解决该问题并提出连续的空间神经语言模型，或者更具体地说是ASGD权重掉落的LSTM语言模型，以及有效地训练它的孟加拉语语言的技术。本文中所示的一些目前基于计数的模型的绩效分析还表明，在孟加拉语持有的数据集中，提出的架构的表现优于其同行。
Language models are generally employed to estimate the probability distribution of various linguistic units, making them one of the fundamental parts of natural language processing. Applications of language models include a wide spectrum of tasks such as text summarization, translation and classification. For a low resource language like Bengali, the research in this area so far can be considered to be narrow at the very least, with some traditional count based models being proposed. This paper attempts to address the issue and proposes a continuous-space neural language model, or more specifically an ASGD weight dropped LSTM language model, along with techniques to efficiently train it for Bengali Language. The performance analysis with some currently existing count based models illustrated in this paper also shows that the proposed architecture outperforms its counterparts by achieving an inference perplexity as low as 51.2 on the held out data set for Bengali.
