What sized character n-grams do they use?
2, 3 and 4
This paper proposes a novel Recurrent Neural Network (RNN) language model that takes advantage of character information. We focus on character n-grams based on research in the field of word embedding construction (Wieting et al. 2016). Our proposed method constructs word embeddings from character n-gram embeddings and combines them with ordinary word embeddings. We demonstrate that the proposed method achieves the best perplexities on the language modeling datasets: Penn Treebank, WikiText-2, and WikiText-103. Moreover, we conduct experiments on application tasks: machine translation and headline generation. The experimental results indicate that our proposed method also positively affects these tasks.
Neural language models have played a crucial role in recent advances of neural network based methods in natural language processing (NLP). For example, neural encoder-decoder models, which are becoming the de facto standard for various natural language generation tasks including machine translation BIBREF1 , summarization BIBREF2 , dialogue BIBREF3 , and caption generation BIBREF4 can be interpreted as conditional neural language models. Moreover, neural language models can be used for rescoring outputs from traditional methods, and they significantly improve the performance of automatic speech recognition BIBREF5 . This implies that better neural language models improve the performance of application tasks. In general, neural language models require word embeddings as an input BIBREF6 . However, as described by BIBREF7 , this approach cannot make use of the internal structure of words although the internal structure is often an effective clue for considering the meaning of a word. For example, we can comprehend that the word `causal' is related to `cause' immediately because both words include the same character sequence `caus'. Thus, if we incorporate a method that handles the internal structure such as character information, we can improve the quality of neural language models and probably make them robust to infrequent words. To incorporate the internal structure, BIBREF7 concatenated character embeddings with an input word embedding. They demonstrated that incorporating character embeddings improved the performance of RNN language models. Moreover, BIBREF8 and BIBREF9 applied Convolutional Neural Networks (CNN) to construct word embeddings from character embeddings. On the other hand, in the field of word embedding construction, some previous researchers found that character INLINEFORM0 -grams are more useful than single characters BIBREF0 , BIBREF10 . In particular, BIBREF0 demonstrated that constructing word embeddings from character INLINEFORM1 -gram embeddings outperformed the methods that construct word embeddings from character embeddings by using CNN or a Long Short-Term Memory (LSTM). Based on their reports, in this paper, we propose a neural language model that utilizes character INLINEFORM0 -gram embeddings. Our proposed method encodes character INLINEFORM1 -gram embeddings to a word embedding with simplified Multi-dimensional Self-attention (MS) BIBREF11 . We refer to this constructed embedding as char INLINEFORM2 -MS-vec. The proposed method regards char INLINEFORM3 -MS-vec as an input in addition to a word embedding. We conduct experiments on the well-known benchmark datasets: Penn Treebank, WikiText-2, and WikiText-103. Our experiments indicate that the proposed method outperforms neural language models trained with well-tuned hyperparameters and achieves state-of-the-art scores on each dataset. In addition, we incorporate our proposed method into a standard neural encoder-decoder model and investigate its effect on machine translation and headline generation. We indicate that the proposed method also has a positive effect on such tasks.