.png){kind=icon}
返回
Tailoring an Interpretable Neural Language Model
.png){kind=icon}
PRE
AI
DOI：10.1109/TASLP.2019.2913087.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Neural networks have shown great potential in language modeling. Currently, the dominant approach to language modeling is based on recurrent neural networks (RNNs) and convolutional neural networks (CNNs). Nonetheless, it is not clear why RNNs and CNNs are suitable for the language modeling task since these neural models are lack of interpretability. The goal of this paper is to tailor an interpretable neural model as an alternative to RNNs and CNNs for the language modeling task. This paper proposes a unified framework for language modeling, which can partly interpret the rationales behind existing language models (LMs). Based on the proposed framework, an interpretable neural language model (INLM) is proposed, including a tailored architectural structure and a tailored learning method for the language modeling task. The proposed INLM can be approximated as a parameterized auto-regressive moving average model and provides interpretability in two aspects: component interpretability and prediction interpretability. Experiments demonstrate that the proposed INLM outperforms some typical neural LMs on several language modeling datasets and on the switchboard speech recognition task. Further experiments also show that the proposed INLM is competitive with the state-of-the-art long short-term memory LMs on the Penn Treebank andWikiText-2 datasets.
Keyword:
Neural language models
interpretability
autoregressive moving average
speech recognition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
5.1
论文数:
2.6K
被引数:
1.1W
机构
引用论文
分享收藏Preparation of molecularly imprinted polymers using ion-pair dummy template imprinting and polymerizable ionic liquids使用离子对虚拟模板印迹和可聚合离子液体制备分子印迹聚合物.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
查看更多内容