2016 LargerContextLanguageModellingw
- (Wang & Cho, 2016) ⇒ Tian Wang, and Kyunghyun Cho. (2016). “Larger-context Language Modelling with Recurrent Neural Network.” In: Proceedings of 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016. ISBN:9781510827585
Subject Headings: Neural Network; Recurrent Neural Networks; Language Modeling; Language Model; Short-Term Memory; Late Fusion; Sentence Context; Memory Augmented Neural Network System.
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Abstract
In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late fusion approach to a recurrent language model based on long short-term memory units (LSTM), which helps the LSTM unit keep intra-sentence dependencies and inter-sentence dependencies separate from each other. Through the evaluation on four corpora (IMDB, BBC, Penn TreeBank, and Fil9), we demonstrate that the proposed model improves perplexity significantly. In the experiments, we evaluate the proposed approach while varying the number of context sentences and observe that the proposed late fusion is superior to the usual way of incorporating additional inputs to the LSTM. By analyzing the trained larger-context language model, we discover that content words, including nouns, adjectives and verbs, benefit most from an increasing number of context sentences. This analysis suggests that larger-context language model improves the unconditional language model by capturing the theme of a document better and more easily.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2016 LargerContextLanguageModellingw | Kyunghyun Cho Tian Wang | Larger-context Language Modelling with Recurrent Neural Network | 2016 |