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[Note: RNNSharp is in maintenance status and won't have new feature any more. For new neural network framework, please try Seq2SeqSharp (https://github.com/zhongkaifu/Seq2SeqSharp)]

RNNSharp

RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above version.

This page introduces what is RNNSharp, how it works and how to use it. To get the demo package, you can access release page.

Overview

RNNSharp supports many different types of deep recurrent neural network (aka DeepRNN) structures.

For network structure, it supports forward RNN and bi-directional RNN. Forward RNN considers histrocial information before current token, however, bi-directional RNN considers both histrocial information and information in future.

For hidden layer structure, it supports LSTM and Dropout. Compared to BPTT, LSTM is very good at keeping long term memory, since it has some gates to contorl information flow. Dropout is used to add noise during training in order to avoid overfitting.

In terms of output layer structure, simple, softmax, sampled softmax and recurrent CRFs[1] are supported. Softmax is the tranditional type which is widely used in many kinds of tasks. Sampled softmax is especially used for the tasks with large output vocabulary, such as sequence generation tasks (sequence-to-sequence model). Simple type is usually used with recurrent CRF together. For recurrent CRF, based on simple outputs and tags transition, it computes CRF output for entire sequence. For sequence labeling tasks in offline, such as word segmentation, named entity recognition and so on, recurrent CRF has better performance than softmax, sampled softmax and linear CRF.

Here is an example of deep bi-directional RNN-CRF network. It contains 3 hidden layers, 1 native RNN output layer and 1 CRF output layer.

Here is the inner structure of one bi-directional hidden layer.

Here is the neural network for sequence-to-sequence task. "TokenN" are from source sequence, and "ELayerX-Y" are auto-encoder's hidden layers. Auto-encoder is defined in feature configuration file. <s> is always the beginning of target sentence, and "DLayerX-Y" means the decoder's hidden layers. In decoder, it generates one token at one time until </s> is generated.

Supported Feature Types

RNNSharp supports many different feature types, so the following paragraph will introduce how these feaures work.

Template Features

Template features are generated by templates. By given templates and corpus, these features can be automatically generated. In RNNSharp, template features are sparse features, so if the feature exists in current token, the feature value will be 1 (or feature frequency), otherwise, it will be 0. It's similar as CRFSharp features. In RNNSharp, TFeatureBin.exe is the console tool to generate this type of features.

In template file, each line describes one template which consists of prefix, id and rule-string. The prefix indicates template type. So far, RNNSharp supports U-type feature, so the prefix is always as "U". Id is used to distinguish different templates. And rule-string is the feature body.

# Unigram
U01:%x[-1,0]
U02:%x[0,0]
U03:%x[1,0]
U04:%x[-1,0]/%x[0,0]
U05:%x[0,0]/%x[1,0]
U06:%x[-1,0]/%x[1,0]
U07:%x[-1,1]
U08:%x[0,1]
U09:%x[1,1]
U10:%x[-1,1]/%x[0,1]
U11:%x[0,1]/%x[1,1]
U12:%x[-1,1]/%x[1,1]
U13:C%x[-1,0]/%x[-1,1]
U14:C%x[0,0]/%x[0,1]
U15:C%x[1,0]/%x[1,1]

The rule-string has two types, one is constant string, and the other is variable. The simplest variable format is {“%x[row,col]”}. Row specifies the offset between current focusing token and generate feature token in row. Col specifies the absolute column position in corpus. Moreover, variable combination is also supported, for example: {“%x[row1, col1]/%x[row2, col2]”}. When we build feature set, variable will be expanded to specific string. Here is an example in training data for named entity task.

Word | Pos | Tag -----------|------|---- ! | PUN | S Tokyo | NNP | S_LOCATION and | CC | S New | NNP | B_LOCATION York | NNP | E_LOCATION are | VBP | S major | JJ | S financial | JJ | S centers | NNS | S . | PUN | S ---empty line--- ! | PUN | S p | FW | S ' | PUN | S y | NN | S h | FW | S 44 | CD | S University | NNP | B_ORGANIZATION of | IN | M_ORGANIZATION Texas | NNP | M_ORGANIZATION Austin | NNP | E_ORGANIZATION

According above templates, assuming current focusing token is “York NNP E_LOCATION”, below features are generated:

U01:New
U02:York
U03:are
U04:New/York
U05:York/are
U06:New/are
U07:NNP
U08:NNP
U09:are
U10:NNP/NNP
U11:NNP/VBP
U12:NNP/VBP
U13:CNew/NNP
U14:CYork/NNP
U15:Care/VBP

Although U07 and U08, U11 and U12’s rule-string are the same, we can still distinguish them by id string.

Context Template Features

Context template features are based on template features and combined with context. In this example, if the context setting is "-1,0,1", the feature will combine the features of current token with its previous token and next token. For instance, if the sentence is "how are you". the generated feature set will be {Feature("how"), Feature("are"), Feature("you")}.

Pretrained Features

RNNSharp supports two types of pretrained features. The one is embedding features, and the other is auto-encoder features. Both of them are able to present a given token by a fixd-length vector. This feature is dense feature in RNNSharp.

For embedding features, they are trained from unlabled corpus by Text2Vec project. And RNNSharp uses them as static features for each given token. However, for auto-encoder features, they are trained by RNNSharp as well, and then they can be used as dense features for other trainings. Note that, the token's granularity in pretrained features should be consistent with training corpus in main training, otherwise, some tokens will mis-match with pretrained feature.

Likes template features, embedding feature also supports context feature. It can combine all features of given contexts into a single embedding feature. For auto-encoder features, it does not support it yet.

Run Time Features

Compared with other features generated offline, this feature is generated in run time. It uses the result of previous tokens as run time feature for current token. This feature is only available for forward-RNN, bi-directional RNN does not support it.

Source Sequence Encoding Feature

This feature is only for sequence-to-sequence task. In sequence-to-sequence task, RNNSharp encodes given source sequence into a fixed-length vector, and then pass it as dense feature to generate target sequence.

Configuration File

The configuration file describes model structure and features. In console tool, use -cfgfile as parameter to specify this file. Here is an example for sequence labeling task:

#Working directory. It is the parent directory of below relatived paths.
CURRENT_DIRECTORY = .

#Network type. Four types are supported:
#For sequence labeling tasks, we could use: Forward, BiDirectional, BiDirectionalAverage
#For sequence-to-sequence tasks, we could use: ForwardSeq2Seq
#BiDirectional type concatnates outputs of forward layer and backward layer as final output
#BiDirectionalAverage type averages outputs of forward layer and backward layer as final output
NETWORK_TYPE = BiDirectional

#Model file path
MODEL_FILEPATH = Data\Models\ParseORG_CHS\model.bin

#Hidden layers settings. LSTM and Dropout are supported. Here are examples of these layer types.
#Dropout: Dropout:0.5 -- Drop out ratio is 0.5 and layer size is the same as previous layer.
#If the model has more than one hidden layer, each layer settings are separated by comma. For example:
#"LSTM:300, LSTM:200" means the model has two LSTM layers. The first layer size is 300, and the second layer size is 200.
HIDDEN_LAYER = LSTM:200

#Output layer settings. Simple, Softmax ands sampled softmax are supported. Here is an example of sampled softmax:
#"SampledSoftmax:20" means the output layer is sampled softmax layer and its negative sample size is 20.
#"Simple" means the output is raw result from output layer. "Softmax" means the result is based on "Simple" result and run softmax.
OUTPUT_LAYER = Simple

#CRF layer settings
#If this option is true, output layer type must be "Simple" type.
CRF_LAYER = True

#The file name for template feature set
TFEATURE_FILENAME = Data\Models\ParseORG_CHS\tfeatures
#The context range for template feature set. In below, the context is current token, next token and next after next token
TFEATURE_CONTEXT = 0,1,2
#The feature weight type. Binary and Freq are supported
TFEATURE_WEIGHT_TYPE = Binary

#Pretrained features type: 'Embedding' and 'Autoencoder' are supported.
#For 'Embedding', the pretrained model is trained by Text2Vec, which looks like word embedding model.
#For 'Autoencoder', the pretrained model is trained by RNNSharp itself. For sequence-to-sequence task, "Autoencoder" is required, since source sequence needs to be encoded by this model at first, and then target sequence would be generated by decoder.
PRETRAIN_TYPE = Embeddin