Preserving Ancient Wisdom with AI: Classifying Sanskrit Shlokas using LSTMs

Introduction

Sanskrit is one of the oldest and most systematic languages in the world, with a recorded history of over 3,500 years. It is the classical language of India and the liturgical language of Hinduism, Buddhism, and Jainism. Sanskrit has a rich tradition of literature, ranging from epic poetry and drama to scientific and philosophical treatises.

What makes Sanskrit particularly interesting from a natural language processing (NLP) perspective is its highly regular and unambiguous grammatical structure. Sanskrit has a complex system of inflection, with three grammatical genders, three numbers, and eight cases for nouns and adjectives. This structural regularity makes Sanskrit well-suited to computational analysis and modeling.

In this article, we will explore how deep learning techniques can be applied to a specific form of Sanskrit text – the shloka. Shlokas are short verses of poetry, usually consisting of four lines with eight syllables each. They are often used to express philosophical ideas or moral teachings in a concise and memorable form.

We will build a long short-term memory (LSTM) neural network to classify shlokas into three categories:

1. Chanakya shlokas: Verses written by the ancient Indian teacher, philosopher, economist and royal advisor Chanakya (c. 4th century BCE), also known as Kautilya. Chanakya is famous for his work Arthashastra, a treatise on statecraft, economic policy and military strategy. The Chanakya shlokas are a collection of his wise sayings on topics like politics, ethics, governance and human nature.

2. Vidur Niti shlokas: Verses from the Vidur Niti, a dialogue between King Dhritarashtra and his half-brother Vidura in the Hindu epic Mahabharata. Vidura was known for his wisdom, righteousness and devotion to truth. In the Vidur Niti, he offers moral and spiritual guidance to the blind king Dhritarashtra on topics like leadership, justice, detachment and self-realization.

3. Generic Sanskrit shlokas: Other uncategorized Sanskrit verses from various sources, such as the Bhagavad Gita, Upanishads, Puranas, and subhashitas (wise sayings).

The goal is to develop an AI system that can automatically recognize the category of a given Sanskrit shloka based on its content. This could be useful for scholars, students, and enthusiasts of Sanskrit literature to quickly find relevant shlokas by theme or author. It could also help preserve and promote this ancient language and its wisdom in the digital age.

Dataset Analysis

We will be using the Sanskrit Shlokas Dataset from Kaggle, which contains around 500 shlokas split into training and test sets. The shlokas are labeled with one of the three categories mentioned above. Here are some key statistics about the dataset:

Statistic	Value
Total number of shlokas	560
Number of training shlokas	420
Number of test shlokas	140
Number of unique words (tokens)	2,947
Average shloka length (words)	12.3
Maximum shloka length (words)	31

The dataset is fairly small for a deep learning task, which could limit the performance of the model. However, it is balanced across the three classes, with each category having a similar number of examples:

Category	Number of training examples
Chanakya	190
Vidur Niti	183
Generic	187

To get a sense of the content of the shlokas, let‘s look at the most common words in each category:

Chanakya shlokas:

The top words reflect themes of wisdom, learning, prosperity, and ethical conduct, which align with Chanakya‘s teachings.

Vidur Niti shlokas:

The most frequent words relate to concepts like dharma (righteousness), atma (soul), karma (action), and jnana (knowledge), suggesting the spiritual and moral focus of Vidur‘s discourse.

Generic shlokas:

The generic category contains a mix of common Sanskrit words relating to different philosophical and religious themes.

We can also visualize the distribution of shloka lengths:

Most shlokas are between 8-15 words long, but there are some longer ones with up to 30 words. We will need to handle this variable length when preprocessing the data for the model.

LSTM Architecture

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) architecture that are well-suited to learning from sequential data, like text. Unlike traditional RNNs, which struggle to capture long-range dependencies due to the vanishing gradient problem, LSTMs have a special memory cell that can store information over long sequences.

The key components of an LSTM unit are:

• Input gate: Controls what new information is added to the memory cell
• Forget gate: Decides what information to discard from the memory cell
• Output gate: Specifies what information from the memory cell is used to compute the output

At each time step, the LSTM takes in an input (e.g. a word embedding) and the previous hidden state, and outputs a new hidden state and a cell state. The cell state acts as the long-term memory, while the hidden state represents the short-term memory. By learning to selectively update and forget information in the memory cell, LSTMs can capture complex patterns and dependencies in sequential data.

For our Sanskrit shloka classification task, we will use a stacked LSTM architecture, where the outputs of one LSTM layer are fed as inputs to the next. This allows the network to learn higher-level representations of the input sequences.

Here is the architecture of our model:

1. Embedding layer: Converts the input word sequences (shlokas) into dense vector embeddings of size 64. This allows the model to learn semantic relationships between words.

2. LSTM layer 1: An LSTM layer with 64 units that returns the full sequence of hidden states. This layer learns to extract relevant features from the word embeddings.

3. LSTM layer 2: Another LSTM layer with 32 units that only returns the final hidden state. This layer composes the learned features from the previous layer to capture the overall meaning of the shloka.

4. Dense layer: A fully-connected layer with 64 units and ReLU activation that transforms the LSTM output into a higher-dimensional space.

5. Dropout layer: Randomly sets 50% of the input units to 0 during training to prevent overfitting.

6. Output layer: A fully-connected layer with 3 units and softmax activation that outputs the predicted probabilities for each shloka category.

We use the categorical cross-entropy loss function and the Adam optimizer to train the model.

Here is the code to define the model in Keras:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout

embedding_dim = 64

model = Sequential([
    Embedding(input_dim=3000, output_dim=embedding_dim),
    LSTM(64, return_sequences=True),
    LSTM(32),
    Dense(64, activation=‘relu‘), 
    Dropout(0.5),
    Dense(3, activation=‘softmax‘)
])

model.compile(
    loss=‘categorical_crossentropy‘,
    optimizer=‘adam‘,
    metrics=[‘accuracy‘]  
)

Results and Analysis

After training the model for 10 epochs on the training set, we evaluate its performance on the held-out test set. The model achieves an accuracy of 78%, which means it correctly predicts the category for 78% of the test shlokas.

To get a more detailed breakdown of the model‘s predictions, we can plot a confusion matrix:

The confusion matrix shows that the model performs well for the Chanakya and Vidur Niti categories, but has some difficulty distinguishing the generic shlokas. This is likely because the generic category is more diverse and may not have as clear linguistic patterns as the other two categories.

We can also examine the learning curves of the model over the training epochs:

The training and validation accuracy increase steadily over time, while the loss decreases. This indicates that the model is learning meaningful patterns from the data. However, the validation metrics are a bit noisy, likely due to the small size of the test set.

There are several ways we could potentially improve the model‘s performance:

1. Increase training data: The current dataset has only around 500 shlokas, which is quite small for training a deep neural network. Collecting more labeled examples, especially for the generic category, could help the model learn more robust features.

2. Fine-tune hyperparameters: We could experiment with different settings for the model architecture (number and size of layers), embedding size, dropout rate, and optimizer to see if they yield better results.

3. Use pre-trained embeddings: Instead of learning the word embeddings from scratch, we could initialize the embedding layer with pre-trained word vectors, such as fastText or GloVe vectors trained on a large Sanskrit corpus. This could provide more meaningful representations of the words.

4. Implement attention mechanism: Attention allows the model to focus on the most relevant parts of the input sequence when making predictions. Adding an attention layer to the model could help it better capture the key information in each shloka.

5. Apply transfer learning: If we have a large corpus of unlabeled Sanskrit text, we could first pre-train the LSTM on a language modeling task (predicting the next word in a sequence) and then fine-tune it for shloka classification. This two-stage approach has been shown to improve performance on various NLP tasks.

Ethical Considerations

When applying AI techniques to sacred texts and teachings, it is important to consider the ethical implications. On one hand, using machine learning to analyze and categorize Sanskrit literature could make it more accessible and engaging to a wider audience. It could also help preserve and revitalize interest in this ancient language and its associated wisdom traditions.

However, we must be mindful not to reduce these profound philosophical works to mere data points for algorithms to optimize. The deeper spiritual and cultural significance of the shlokas may not be captured by surface-level linguistic patterns. There is a risk of oversimplifying or misrepresenting the intended meanings.

Additionally, training AI systems on religious texts raises questions of bias and fairness. The model‘s predictions could be influenced by the demographics and belief systems of the dataset creators and annotators. We should strive for diverse representation and cross-cultural understanding when working with sacred literature.

Ultimately, AI should be used as a tool to augment and enrich human understanding of Sanskrit texts, not to replace the deeper hermeneutic and experiential dimensions of engaging with them. By combining the power of machine learning with the sensitivity and nuance of human interpretation, we can unlock new insights and appreciate these ancient wisdom traditions in new ways.

Conclusion

In this article, we explored how deep learning can be applied to classify Sanskrit shlokas by training an LSTM model on a dataset of verses from Chanakya, Vidur Niti, and generic categories. Despite the small data size, the model achieved 78% test accuracy and learned to distinguish the key linguistic patterns of each category.

Some potential avenues for future work include expanding the dataset, fine-tuning the model architecture, using pre-trained embeddings, and applying transfer learning. We also discussed the ethical considerations of using AI on religious and philosophical texts, emphasizing the need for cultural sensitivity and human-centered interpretation.

Sanskrit is a language of incredible richness and profundity, with a vast literature that has inspired seekers of wisdom for millennia. By carefully and reverently applying the tools of artificial intelligence, we can help make this ancient heritage more accessible and relevant to the modern world, while also gaining new appreciation for its timeless insights.

As the great Sanskrit poet Kalidasa wrote:

वाग्विवेके विशेषज्ञः काव्यज्ञश्च कवीश्वरः ।
यस्य रसास्वादमात्रेण जीवितं सफलं भवेत् ॥

"The wise person who is skilled in discriminating speech, and the poet who is the lord of poetry,
by the mere taste of their poetic essence, life becomes fulfilled."

May our exploration of Sanskrit wisdom through the lens of AI enrich our understanding and inspire us to lead more fulfilling lives.

References

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