Répondre automatiquement aux incidents IT par le Deep Learning et proposition d’une nouvelle approche question answering basée sur SpanBERT et LSTM
Abstract #
Question answering has become one of the main objectives of companies. It automatically
provides immediate answers to problems that a user may encounter when interacting with a
service offered by a company. Traditional methods of incident response have several
limitations that can impose damage and loss, and which can lead to critical shutdowns of
business processes. In this regard, the host organization « Grupo AVALON » which operates
in the IT field has decided to invest in artificial intelligence, intending to implement a question
answering solution to answer automatically to IT incidents reported by its customers and
employees. Therefore, to meet the needs of the host organization, we proposed a solution
based on the latest models that marked the new era of natural language processing to provide
rapid, automatic, and correct responses to incidents, through the use of textual data.
On the other hand, for a precise definition of the objectives, we carried out a study and a
needs analysis, with the intention of best meeting the expectations concerning the project.
Then, we were inspired by a recent literature review, which allowed us to analyze the different
scientific approaches adopted to solve the problem, namely, Deep Learning models and
models based on transformers like BERT and its variants.
Finally, we developed and compared the Deep Learning models, namely, BETO, BERT,
mBERT, SpanBERT, then we proposed new approaches based on the hybridization of these
models. In particular, BETO-LSTM, BETO-BiLSTM, BERT-LSTM, BERT-BiLSTM, SpanBERTLSTM, SpanBERT-BiLSTM, SpanBERTLarge-LSTM, and SpanBERTLarge-LSTM and this, for two
types of datasets. To conclude, that the BETO model is the one that best models the incident
dataset with an F1 score of 88.77%. Then, thanks to our approach, we were able to exceed
and outperform the advanced models, namely, BERT and its variant SpanBERT, by our
SpanBERTLarge-LSTM model which gave the best result with an Exact Match of 84.5094% on
the benchmarking question answering dataset SQuaD.