From Encoder-decoder to Attention Mechanism

Lets start with understanding What encoder & decoder means in the world of natural language.

Imagine you're trying to translate a sentence from English to French. The encoder-decoder model works like a team of two people: the encoder and the decoder.

The encoder's job is to understand the input sentence in English and summarize its meaning into a compact representation. It reads each word of the English sentence, analyzes their relationships, and captures the important information in a fixed-length "summary" or "context" vector. It's like someone listening to you speak in English and taking notes to understand the main idea.

Once the encoder has created this summary, it passes it to the decoder. The decoder takes the summary and uses it as a starting point to generate the translated sentence in French. It does this by considering the context vector along with its own internal knowledge and vocabulary. It starts with the first word of the translation and uses its understanding to predict the next word, and so on. It's like someone using the notes from the encoder to construct a meaningful response in French.

Little context behind RNNs - Imagine you're reading a book, and you want to understand the story. As you read each word, you not only process the current word but also use your understanding of the previous words to make sense of the story. You continuously build context and remember information from earlier parts of the book. This is similar to how RNNs work.

Together, the encoder and decoder work collaboratively to convert the input sentence in one language (English) into the output sentence in another language (French). The encoder captures the essence of the input, and the decoder uses that information to produce an accurate and coherent translation.

The encoder-decoder architecture is widely used not only in machine translation but also in various other tasks like text summarization, chatbot responses, Language Generation, Question-Answering Systems, Image Captioning, Speech Recognition and Conversational AI and more.

Okay now lets understand what the foundational problem with above architecture.

One of the challenges with the traditional encoder-decoder architecture is the issue of information compression or loss of information during the encoding process. Let's delve into this problem in more detail:

In the encoder-decoder architecture, the input sequence is typically encoded into a fixed-length representation (context vector) by the encoder. This fixed-length representation is then used by the decoder to generate the output sequence. However, compressing the entire input sequence into a fixed-length vector can lead to the loss of fine-grained details and subtle nuances present in the input.

For example, in machine translation, if a long input sentence needs to be translated, important contextual information from the beginning of the sentence might get diluted or completely lost in the fixed-length representation. As a result, the decoder may struggle to accurately generate the corresponding output sequence.

To address this problem, an enhancement called the attention mechanism was introduced.

Lets understand how it works by this example.

Imagine you have a customer review that says, "The food at this restaurant is amazing, but the service needs improvement." You want to use a machine learning model to determine the overall sentiment of this review.

With the attention mechanism, the model can focus on different parts of the review as it predicts the sentiment. For example, when determining the sentiment associated with "amazing," the model might assign higher attention weights to this positive word, indicating its importance in determining the positive sentiment. Similarly, when analyzing the sentiment related to "needs improvement," the attention mechanism might assign higher weights to this phrase, highlighting its influence on the negative sentiment.

By attending to different parts of the review, the attention mechanism allows the model to consider the important words and phrases that contribute to the sentiment. It helps the model focus on the key aspects of the text and weigh them accordingly when making sentiment predictions.

In this case, the attention mechanism helps the model "pay attention" to the relevant words and phrases.

Attention can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering etc.

Thanks for reading , in coming edition I am going to talk about evolution of language models and basics of Transformers & Bert Architecture which has revolutionized the field of NLP. #nlp #encoder #decoder #attention #textanalytics #datascience #artificialintelliegence