Improving Conversational AI: A Comparative Analysis of LSTM and LSTM with Attention Mechanisms

Siddharth Chandel
Lovely Professional University, Phagwara (PB)
Email: siddharthchandel2004@gmail.com
LinkedIn: siddharth-chandel-001097245
Github repo: Conversational-AI

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1. Introduction

The development of conversational agents, commonly referred to as chatbots, has made significant strides in recent years, largely due to advancements in deep learning technologies. Among these, Recurrent Neural Networks (RNNs) and their variant, Long Short-Term Memory (LSTM) networks, have emerged as powerful tools for various natural language processing (NLP) tasks.

Traditional LSTM models have proven effective in processing sequential data, but they struggle with long-term dependencies, leading to less coherent responses. The Attention mechanism enhances LSTM models by dynamically focusing on important parts of the input.

This paper explores the impact of the Attention mechanism on LSTM networks in conversational AI applications.

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2. Related Work

Several studies have explored Recurrent Neural Networks (RNNs) and LSTMs for dialogue generation. The Seq2Seq model [1] laid the foundation for chatbots, while Bahdanau et al. [2] introduced the Attention mechanism, improving performance in text generation tasks.

Further advancements include Luong et al. [3], who refined Attention with global and local attention, and Vaswani et al. [4], who introduced the Transformer model, eliminating the need for RNNs altogether.

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3. Methodology

3.1 Dataset

A custom question-answer dataset was used, formatted as conversational exchanges. Preprocessing steps included:

• Tokenization: Limited to the 1,000 most frequent words.
• Padding: Standardized sequences to 20 tokens.

3.2 Model Architectures

Two models were compared: Baseline LSTM and LSTM with Attention.

3.2.1 Baseline LSTM Model

A standard encoder-decoder LSTM:

• Encoder: Converts input into a context vector.
• Decoder: Generates output responses.

Hyperparameters:

• Embedding Dimension: 64
• LSTM Units: 128
• Vocabulary Size: 1,000

3.2.2 LSTM with Attention

An Attention mechanism was added:

• Attention Layer: Computes weighted context vectors.
• Concatenation Layer: Merges attention-enhanced context into the decoder.

Same hyperparameters as the baseline model for fair comparison.

3.3 Training Procedure

• Epochs: 100
• Batch Size: 64
• Loss Function: Sparse categorical cross-entropy
• Optimizer: Adam

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4. Experiments and Results

4.1 Training and Evaluation

Training & Validation Loss:

Findings:

• Lower validation loss for LSTM with Attention indicates better generalization.
• The Attention mechanism improved response relevance.

4.2 Response Quality Analysis

Example responses:

Input: "Hi, how are you doing?"

• Baseline LSTM: "I'm doing about about 90 of"
• LSTM with Attention: "I'm fine, how about yourself?"

The LSTM with Attention produced more coherent responses.

4.3 Attention Weights Visualization

Attention distribution for query: "How are you doing today?"

• Response: "I'm doing great, what about you?"
• The model focused on "how" and "doing", improving response relevance.

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5. Discussion

• Baseline LSTM struggles with long-term dependencies.
• LSTM with Attention dynamically focuses on important words, improving coherence.
• Attention-based models are more context-aware, leading to a better user experience.

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6. Conclusion and Future Work

The LSTM with Attention outperformed the baseline LSTM, generating contextually accurate responses.

Future work:

• Explore transformer-based models (e.g., BERT, GPT).
• Optimize Attention mechanisms.
• Train on larger datasets.

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7. References

1. Serban, I. V., et al. (2016). Building end-to-end dialogue systems using generative hierarchical neural network models.
2. Bahdanau, D., et al. (2014). Neural machine translation by jointly learning to align and translate.
3. Luong, M. T., et al. (2015). Effective approaches to attention-based neural machine translation.
4. Vaswani, A., et al. (2017). Attention is all you need.