Understanding Sequence Models: Bridging Gaps in Prediction
In the realm of machine learning and natural language processing (NLP), understanding sequences is paramount. A sequence can be anything from a sentence in language, to medical signals, or even the waveform of speech. At its core, a sequence is an ordered collection of elements that conveys meaningful information. Consider the task of predicting the next word in a sentence, a classic sequence modeling problem. Given the context of a sentence like "This morning I took the dog for a walk," predicting the next word requires understanding the sequential flow of language. One approach is to use a fixed window, considering a subset of words to predict the next one. However, this method has limitations, particularly in capturing long-term dependencies. For instance, in a sentence like "In Finland, I had a great time and I learnt some of the _________ language," accurately predicting the missing word necessitates understanding information from both distant past and future w...