Human dialogues often show underlying dependencies between turns, with each interlocutor influencing the queries/responses of the other. This article follows this by proposing a neural architecture for conversation modeling that looks at the dialogue history of both sides. It consists of a generative model where one encoder feeds three decoders to process three successive turns of dialogue for predicting the next utterance, with a multidimension attention mechanism aggregating the past and current contexts for a cascade effect on each decoder. As a result, a more comprehensive account of the dialogue evolution is obtained than by focusing on a single turn or the last encoder context, or on the user side alone. The response generation performance of the model is evaluated on three corpora of different sizes and topics, and a comparison is made with six recent generative neural architectures, using both automatic metrics and human judgments. Our results show that the proposed architecture equals or improves the state-of-the-art for adequacy and fluency, particularly when large open-domain corpora are used in the training. Moreover, it allows better tracking of the dialogue state evolution for response explainability.

We present a generative neural model for open and multi-turn dialog response generation that relies on a multi-dimension attention process to account for the semantic interdependence between the generated words and the conversational history, so as to identify all the words and utterances that influence each generated response. The performance of the model is evaluated on the wide scope DailyDialog corpus and a comparison is made with two other generative neural architectures, using several machine metrics. The results show that the proposed model improves the state of the art for generation accuracy, and its multi-dimension attention allows for a more detailed tracking of the influential words and utterances in the dialog history for response explainability by the dialog history.

Chatbots or conversational agents have enjoyed great popularity in recent years. They surprisingly perform sensitive tasks in modern societies. However, despite the fact that they offer help, support, and fellowship, there is a task that is not yet mastered: dealing with complex emotions and simulating human sensations. This research aims to design an architecture for an emotional conversation agent for long-text conversations (multi-turns). This agent is intended to work in areas where the analysis of users feelings plays a leading role. This work refers to natural language understanding and response generation.

Emotion recognition and mining tasks are often limited by the availability of manually annotated data. Several researchers have used emojis and specific hashtags as forms of training and supervision.This research paper proposes a new textual and social corpus, the corpus labeled using basic emotions following Plutchik's theory. Thus, This paper propose a first study for the representation and interpretation of complex emotional interactions, using deep neural networks.

In traditional text classification, classes are mutually exclusive, i.e. it is not possible to have one text or text fragment classified into more than one class. On the other hand, in multi-label classification an individual text may belong to several classes simultaneously. This type of classification is required by a large number of current applications such as big data classification, images and video annotation. Supervised learning is the most used type of machine learning in the classification task. It requires large quantities of labeled data and the intervention of a human tagger in the creation of the training sets. When the data sets become very large or heavily noisy, this operation can be tedious, prone to error and time consuming. In this case, semi-supervised learning, which requires only few labels, is a better choice. In this paper, we study and evaluate several methods to address the problem of multi-label classification using semisupervised learning and data from social networks. First, we propose a linguistic pre-processing involving tokenisation, recognition of named entities and hashtag segmentation in order to decrease the noise in this type of massive and unstructured real data and then we perform a word sense disambiguation using WordNet. Second, several experiments related to multi-label classification and semi-supervised learning are carried out on these data sets and compared to each other. These evaluations compare the results of the approaches considered. This paper proposes a method for combining semisupervised methods with a graph method for the extraction of subjects in social networks using a multi-label classification approach. Experiments show that the performance of the proposed model increases in 4 p.p. the precision of the classification when compared to a baseline.

The phenomenon of big data is described using five Vs: Volume, Variety, Velocity, Variability and Veracity. In this paper, we are interested by analyzing and pre-processing tweets for NLP and machine learning applications such as machine translation and classification. Collected contents from Twitter (tweets) are considered as unstructured, highly noisy and short (140 characters) texts. Overcoming these complex challenges will help learn from such data and apply traditional NLP and machine learning techniques. In this paper, we propose a pre-processing pipeline for tweets consisting of filtering part-of-speech, named entities recognition, hashtag segmentation and disambiguation. Our proposed approach is also based on the graph theory and group words of tweets using semantic relations of WordNet and the idea of connected components. Evaluations on the task of classification showed promising results when using this proposed preprocessing pipeline, with an increase in the accuracy of the classification up to 87.6%.