Towards a Fully Unsupervised Framework for Intent Induction in Customer Support Dialogues

State of the art models in intent induction require annotated datasets. However, annotating dialogues is time-consuming, laborious and expensive. In this work, we propose a completely unsupervised framework for intent induction within a dialogue. In addition, we show how pre-processing the dialogue corpora can improve results. Finally, we show how to extract the dialogue flows of intentions by investigating the most common sequences. Although we test our work in the MultiWOZ dataset, the fact that this framework requires no prior knowledge make it applicable to any possible use case, making it very relevant to real world customer support applications across industry.Comment: 16 pages, 8 figure

Deep Clustering: A Comprehensive Survey

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering

User chat clustering using deep learning representations and unsupervised methods for dialog system applications

Os sistemas automáticos de conversação, conhecidos normalmente como chat bots, estão a tornar-se cada vez mais populares e devem ser capazes de interpretar a linguagem humana para compreender e comunicar com os seres humanos. A deteção de intenções desempenha uma tarefa crucial para desenvolver conversas inteligentes nestes sistemas de conversa. As implementações existentes destes sistemas requerem muitos dados etiquetados e a sua aquisição pode ser dispendiosa e demorada. Esta tese visa avaliar representações de texto existentes, utilizando abordagens clássicas, tais como Word2Vec, GloVe e modelos de Transformer pré-treinados (BERT, RoBERTa, GPT2 e outros), para possível automatização de dados de diálogo não etiquetados através de algoritmos de agrupamento. Os algoritmos de agrupamento testados, vão desde o clássico K-Means até abordagens mais sofisticadas, tais como HDBSCAN, com a ajuda de técnicas de redução de dimensão (t-SNE, UMAP). Um conjunto de dados é utilizado para avaliação das técnicas utilizadas, que contêm diálogo de intents de utilizadores em múltiplos domínios e taxonomia de intents variada que se encontram no mesmo domínio. Os resultados mostram que os Transformers apresentam um desempenho de representação de texto superior às representações clássicas. No entanto, um modelo ensemble com múltiplos algoritmos de agrupamento e de múltiplas representações de fontes diferentes apresenta uma melhoria drástica na solução final. A aplicação do UMAP e t-SNE em dimensões mais baixas pode também apresentar um desempenho tão bom ou mesmo melhor do que as representações originais.Dialog systems commonly called chat bots are increasingly more popular and must interpret spoken language to understand and communicate with humans. Intent detection plays a crucial task to develop smart and intelligent conversations in these conversational systems. Existing implementations require a lot of labeled data and acquiring it can be costly and time-consuming. This thesis aims to evaluate existing text representations, using classical approaches, such as Word2Vec, GloVe, and current state of the art pre-trained Transformer models (BERT, RoBERTa, GPT2, and more) for possible automation of unlabeled dialog data through clustering algorithms. The cluster algorithms tested, range from the classical K-Means to more sophisticated approaches such as HDBSCAN, with dimension reduction techniques (t-SNE, UMAP) as pre processing techniques. A dataset is used for evaluation that contains multiple user intents in many domains and varying intents taxonomy in the same domain. Results show that Transformers demonstrate superior text representation performance to classical representations. Nevertheless, ensemble clustering with multiple clustering algorithms and multiple representations from different sources shows massive improvement in the final clustering solution. Applying UMAP and t-SNE in lower dimensions may also perform as good or even better than the original clustering with the original embeddings