By Daniel Benito, Chief Product Officer at Bitext When implementing conversation flows for a chatbot, one of the most time-consuming tasks is preparing training data for the Natural Language Understanding (NLU) component. This involves coming up with examples of the...
The impact of lemmatization for morphologically-rich languages Abstract Are there ways to improve the performance of language models, beyond increases in size -both in the number of model parameters or in the size of training corpora? Our benchmarks show that another...
The use of word embeddings has become the standard approach for dealing with text input in AI models. While an extensive research has been carried out during these years to analyze all theoretical underpinnings of algorithms such as word2vec, fastText and BERT, it is...
In recent years, word embeddings have become the de facto input layer for virtually all AI-based NLP tasks. While they have undoubtedly allowed text-based AI to advance significantly, not much effort has gone towards examining the limitations of using them in...
While a lot of research has been devoted to analyzing the theoretical basis of word embeddings, not as much effort has gone towards examining the limitations of using them in production environments Quick intro. Word embeddings are essentially a way to convert text...
Recent Comments