## 1 Introduction  
Spoken language understanding (SLU) in conversational systems is traditionally divided into two main subtasks: intent detection and semantic slot filling, both extended with domain recognition for multidomain dialogue systems [1, 2]. Intent detection or recognition (sometimes also called intent classification) is the task of classifying user utterances into previously defined intent categories. This classification is based on what the user wants to achieve [3, 4]. These users' intents can also be denoted as dialogue acts, which are often defined as update operations on information states (or contexts). These state-changing operations or actions are produced and updated by the users share in the dialogue to fulfill their needs, wants, and purposes (e.g., ask for train schedules, book a hotel, etc.). Intent classification is a complex process that normally involves several key challenges. First, adequate data sources to apply statistical methodologies are often not available (very few public corpora have intent annotations, which indeed may be difficult and time-consuming to obtain) [5]. Another important challenge is the irregularity of users' expressions. Users tend to use colloquial expressions and short sentences, which may cause identifying user's intents to be very difficult [6]. Finally, the use of broad content, implicit intents (i.e., when the user does not have clear intent requirements, and therefore the user's intent must be inferred from the set of possible intents defined for the task), or multiple intents (i.e., when the user refers to more than one intent) are other examples of challenging situations that cause intent detection to be particularly difficult [7; 8]. Intent classification can be particularly useful for conversational systems to empower customer services with AI-driven FAQ software [1]. Its usefulness in this domain is twofold: first, the intent recognizer may help detecting the main information pieces that are present in the user's utterances, thus becoming a solution for completing the natural language understanding task; then, similarly, the dialogue management task could also be accomplished by simply assigning the user utterance to one of the intents defined, the one corresponding to the adequate response from the system. In this paper we present an exploratory analysis in which different deep learning (DL) models for intent detection and classification were evaluated. Particularly, we compared and analyzed traditional RNN and transformer models, an emergent family of models that achieve state-of-the-art performance in numerous natural language processing applications. Our first approach is based on the typical recurrent neural network model with word embeddings as its first layer. The results of the evaluation of this model shows an improvement of performance when using named-entities for intent recognition (as an alternative to using raw text with basic preprocessing) and embeddings adapted to the specific task (compared to using \"out-of-the-box\" embeddings trained on general text corpora). Our second approach is based on a transformer model that was developed by fine-tuning a BETO model (a Spanish pretrained bidirectional encoder representations from transformers (BERT) model from the Universidad de Chile) for the intent detection task. Our experiments in this case confirmed that best performance was achieved using transformer models. In addition to the comparison between models, we also analyze the effect of inserting unseen domain words to extend the vocabulary of the model as part of the fine-tuning or domain-adaptation process. In this regard, transformers can be quite inefficient in learning new domain words, particularly those that are not backed up with sufficient domain-specific training data. This suggests that it might be preferable to not include every unseen word but just those that are best for bootstrapping their embeddings from the available data.