Machine Transliteration ( English to Hindi ) Tensorflow

In this project, we have built a character level language model for transliterating English text into Hindi. It is implemented using TensorFlow. Transliteration is the phonetic translation of the words in a source language ( here English ) into equivalent words in a target language ( here Hindi ). It preserves the pronunciation of the words.
This project has been inspired by the NMT model developed by deep-diver.

Note: Refer to GUIDE.md for a brief on code implementation.

Overview of the Architecture

Transliteration being a type of many to many problem, we built a encoder-decoder model in TensorFlow. The objective of the model is Transliterating English text to Hindi script.

• Dataset: We have used FIRE 2013 dataset to train the model. FIRE dataset is useful for transliteration tasks, the one we used contains 30,823 word transliteration pairs of English to Hindi.
• Exploring the data: The data has been stored separately into two variables; source text and target text i.e. English and Hindi respectively

Preprocessing:

The preprocessing contains two important steps which include:

• Creating lookup tables: We made dictionaries (i.e. mapping tables) of character to corresponding character id and vice versa for both the source and target characters (vocabulary). Now we have 4 lookup tables.
• Text to character ids: We converted each character in the list of words to the corresponding index with the help of look-up tables The pre-processed data is saved to the external file.

Building the Neural Network:

We then created a sequence to sequence model i.e. Encoder-Decoder layers.

1. Inputs to the encoding layer:

   • enc_dec_model_inputs() : we have defined a function to create placeholders for encoder-decoder inputs.
   • hyperparam_inputs() : to create placeholders for the hyper parameters that are needed later, lr_rate (learning rate) and keep_prob (probability of dropouts).

2. Encoder: It contains two parts: Embedding layer & RNN layer. The former converts each character in the word with the number of features specified as encoding_embedding_size. The later part being the RNN layer, we have used LSTM cells stacked together after dropout technique.

3. Decoder: Decoding model mainly comprises of two phases, Training and Inference. Both of them share the same architecture and parameters, but the difference comes in feeding the shared model.

• Initially we preprocess the target label data for the training phase, i.e. we add a special token <GO> in front of all target data to imply the start of transliteration.
• While passing the embedding layer to the decoder, we cannot use tf.contrib.layers.embed_sequence like in encoder. The reason being, we need to pass the embedding layer to both training and inference phases and tf.contrib.layers.embed_sequence embeds the prepared dataset before running, but in inference phase we need the dynamic embedding capability.
• In decoder-training-phase, the embeded input is passed as input in each time step. Whereas in decoder-inference-phase, the the output of the previous time-step is dynamically passed over embedding parameters and fed to the next time step. tf.variable.scope() is used for the sharing of parameters and variables between training and inference processes since they both share the same architecture.

To build the seq2seq model:

Here, the previously created layers, encoding_layer, process_decoder_input and decoding_layer are put together to build the full-fledged Sequence to Sequence model.

Build the Graph:

1. Cost Function: tf.contrib.seq2seq.sequence_loss is used for calculating loss function i.e., a weighted softmax cross entropy loss function. Weights are explicitly provided as an argument, and it can be created by tf.sequence_mask.

2. Optimizer: We used Adam optimizer (tf.train.AdamOptimizer) with specified learning rate.

3. Gradient Clipping: tf.clip_by_value is used to do the gradient clipping to overcome exploding gradient.

Train:

We defined get_accuracy function to compute train and validation accuracy.

Save parameters:

We then saved the batch_size and save_path parameters for inference.

Transliterate:

We then defined a function word_to_seq to do the transliteration of input words by the user.

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