Enhancing Intent Detection and Slot Filling with Multitask Learning

In this paper, the authors propose XLNet, a novel language model that leverages autoregressive pre-training to improve language understanding. Unlike traditional language models that focus on generating text, XLNet is designed to predict the next word in a sequence given the context of the previous words. This approach allows the model to learn the dependencies and relationships between words in a sentence, leading to better performance in various natural language processing (NLP) tasks.
The authors begin by discussing the challenges of training language models, particularly the "masked language modeling" task where a portion of the input sequence is randomly replaced with a special token. They argue that this approach can lead to overfitting and poor generalization, as the model is trained to predict the missing token rather than the actual words in the sequence. To address this issue, XLNet introduces a "generalized autoregressive pre-training" framework that uses a combination of unsupervised and supervised learning techniques.
The key innovation of XLNet is its use of a multi-layer bidirectional transformer encoder to generate contextualized representations of the input sequence. These representations are then used to predict the next word in the sequence through an autoregressive decoding process. The authors show that this approach can achieve state-of-the-art performance on a variety of NLP tasks, including language translation and question answering.
To further improve the performance of XLNet, the authors introduce a new technique called "slot-to-intent attention." This mechanism allows the model to focus on the most relevant intent information when predicting the next word in a sequence, leading to better performance in tasks such as natural language inference and text classification.
Overall, the authors demonstrate that XLNet achieves significant improvements over traditional language models by using autoregressive pre-training and slot-to-intent attention. These advances have important implications for a wide range of NLP applications, including chatbots, virtual assistants, and language translation systems.