Review: Character-Aware Neural Language Models

Character-Level Language Model Using CNN and Highway

(Figure from their presentation slides)

In this story, Character-Aware Neural Language Models, (LSTM-Char-CNN), by Harvard University, and New York University, is reviewed. In this paper:

• Instead of word-level language model, a CNN and a highway network are used over characters, whose output is given to a LSTM recurrent neural network language model (RNN-LM).
• 60% fewer parameters is achieved.

This is a paper in 2016 AAAI with over 1700 citations. (Sik-Ho Tsang @ Medium)

Outline

1. Notations Related to Language Model
2. Proposed Model Architecture
3. Experimental Results

1. Notations Related to Language Model

• (I think this paper gives a very clear notations related to language model.)
• Let V be the fixed size vocabulary of words.

A language model specifies a distribution over wt+1 (whose support is V) given the historical sequence w1:t = [w1, …, wt].

1.1. Output

• A recurrent neural network language model (RNN-LM) is used by applying an affine transformation to the hidden layer (ht) followed by a softmax:

• where pj is the j-th column of P, which is referred to as the output embedding.

1.2. Input

• For a conventional RNN-LM which usually takes words as inputs, if wt=k, then the input to the RNN-LM at t is the input embedding xk, the k-th column of the embedding matrix X.

1.3. Negative Log-Likelihood (NLL)

• If we denote w1:T = [w1, …, wT] to be the sequence of words in the training corpus, training involves minimizing the negative log-likelihood (NLL) of the sequence:

1.4. Perplexity (PPL)

• Perplexity (PPL) is used to evaluate the performance of our models. Perplexity of a model over a sequence [w1, …, wT] is given by:

The proposed model here simply replaces the input embeddings X with the output from a character-level CNN, to be described below.

2. Proposed Model Architecture

Architecture of the proposed language model applied to an example sentence

2.1. Input & CharCNN

CharCNN

• The input at time t is an output from a character-level convolutional neural network (CharCNN).
• Let C be the vocabulary of characters, d be the dimensionality of character embeddings.
• Suppose that word k is made up of a sequence of characters [c1, …, cl], where l is the length of word k. Then the character-level representation of k is given by the matrix Ck.
• CharCNN:

• where <Ck, H> is the convolution with kernel H.
• Then a bias is added and a nonlinearity tanh is applied to obtain a feature map fk.
• CharCNN uses multiple filters of varying widths to obtain the feature vector for k..

2.2. Max Over Time

Max Over Time

CharCNN & Max Over Time (Figure from their presentation slides)

• Finally, the max-over-time is used:

• This is the feature corresponding to the filter H (when applied to word k). The idea is to capture the most important feature, which is the one with the highest value for a given filter.

2.3. Highway

Highway

• In convention, one layer of an MLP can be applied:

• where g is a nonlinearity.
• In this paper, one layer of highway network is used:

• where t is:

• where t is transform gate, and (1-t) is carry gate.

Similar to the memory cells in LSTM networks, highway layers allow for training of deep networks by adaptively carrying some dimensions of the input directly to the output.

• (Please read Highway if interested.)

2.5. LSTM

LSTM

• Finally, LSTM is used with z as input, and output the next word.

2.4. Two Networks (LSTM-Char-Small and LSTM-Char-Large)

Architecture of the small and large model

• One small model and one large model are designed, which are called LSTM-Char-Small and LSTM-Char-Large models respectively.
• d = dimensionality of character embeddings; w = filter widths;
• h = number of filter matrices, as a function of filter width (so the large model has filters of width [1; 2; 3; 4; 5; 6; 7] of size [50; 100; 150; 200; 200; 200; 200] for a total of 1100 filters);
• f; g = nonlinearity functions; l = number of layers; m = number of hidden units.
• Two datasets are used for training. Small one is DATA-L, big one is DATA-S.
• Hierarchical Softmax is used for DATA-L, which is a common strategy for large dataset:

• where V is randomly split into mutually exclusive and collectively exhaustive subsets V1, …, Vc. r is the cluster index.
• The first term is simply the probability of picking cluster r, and the second term is the probability of picking word j given that cluster r is picked.

3. Experimental Results

3.1. English Penn Treebank

Performance of the proposed model versus other neural language models on the English Penn Treebank test set

The proposed large model is on par with the existing state-of-the-art (Zaremba et al. 2014), despite having approximately 60% fewer parameters.

• The proposed small model significantly outperforms other NLMs of similar size.

3.2. Other Languages

Test set perplexities for DATA-S

The character-level models outperform their word-level counterparts.

Test set perplexities for DATA-L

• Due to memory constraints, only the small models on DATA-L are trained.
• Interestingly, no significant differences are observed going from word to morpheme LSTMs on Spanish, French, and English.

The character models again outperform the word/morpheme models.

• (Please feel free to read paper if interested in the ablation studies.)

References

[2016 AAAI] [LSTM-Char-CNN]
Character-Aware Neural Language Models
Their Slides: https://people.csail.mit.edu/dsontag/papers/kim_etal_AAAI16_slides.pdf

Natural Language Processing (NLP)

Language/Sequence Model: 2007 [Bengio TNN’07] 2013 [Word2Vec] [NCE] [Negative Sampling] 2014 [GloVe] [GRU] [Doc2Vec] 2015 [Skip-Thought] 2016 [GCNN/GLU] [context2vec] [Jozefowicz arXiv’16] [LSTM-Char-CNN] 2017 [TagLM]
Machine Translation: 2014 [Seq2Seq] [RNN Encoder-Decoder] 2015 [Attention Decoder/RNNSearch] 2016 [GNMT] [ByteNet] [Deep-ED & Deep-Att] 2017 [ConvS2S] [Transformer]
Image Captioning: 2015 [m-RNN] [R-CNN+BRNN] [Show and Tell/NIC] [Show, Attend and Tell]

My Other Previous Paper Readings