HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings

We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings and role labeling by combining these embeddings with syntactical features. A simple combination of these steps shows very competitive results and can be extended to process other datasets and languages.Comment: 5 pages, 3 tables, accepted at SemEval 201

Probabilistic Relational Model Benchmark Generation

The validation of any database mining methodology goes through an evaluation process where benchmarks availability is essential. In this paper, we aim to randomly generate relational database benchmarks that allow to check probabilistic dependencies among the attributes. We are particularly interested in Probabilistic Relational Models (PRMs), which extend Bayesian Networks (BNs) to a relational data mining context and enable effective and robust reasoning over relational data. Even though a panoply of works have focused, separately , on the generation of random Bayesian networks and relational databases, no work has been identified for PRMs on that track. This paper provides an algorithmic approach for generating random PRMs from scratch to fill this gap. The proposed method allows to generate PRMs as well as synthetic relational data from a randomly generated relational schema and a random set of probabilistic dependencies. This can be of interest not only for machine learning researchers to evaluate their proposals in a common framework, but also for databases designers to evaluate the effectiveness of the components of a database management system

Structured Dialogue State Management for Task-Oriented Dialogue Systems

Human-machine conversational agents have developed at a rapid pace in recent years, bolstered through the application of advanced technologies such as deep learning. Today, dialogue systems are useful in assisting users in various activities, especially task-oriented dialogue systems in specific dialogue domains. However, they continue to be limited in many ways. Arguably the biggest challenge lies in the complexity of natural language and interpersonal communication, and the lack of human context and knowledge available to these systems. This leads to the question of whether dialogue systems, and in particular task-oriented dialogue systems, can be enhanced to leverage various language properties. This work focuses on the semantic structural properties of language in task-oriented dialogue systems. These structural properties are manifest by variable dependencies in dialogue domains; and the study of and accounting for these variables and their interdependencies is the main objective of this research. Contemporary task-oriented dialogue systems are typically developed with a multiple component architecture, where each component is responsible for a specific process in the conversational interaction. It is commonly accepted that the ability to understand user input in a conversational context, a responsibility generally assigned to the dialogue state tracking component, contributes a huge part to the overall performance of dialogue systems. The output of the dialogue state tracking component, so-called dialogue states, are a representation of the aspects of a dialogue relevant to the completion of a task up to that point, and should also capture the task structural properties of natural language. Here, in a dialogue context dialogue state variables are expressed through dialogue slots and slot values, hence the dialogue state variable dependencies are expressed as the dependencies between dialogue slots and their values. Incorporating slot dependencies in the dialogue state tracking process is herein hypothesised to enhance the accuracy of postulated dialogue states, and subsequently potentially improve the performance of task-oriented dialogue systems. Given this overall goal and approach to the improvement of dialogue systems, the work in this dissertation can be broken down into two related contributions: (i) a study of structural properties in dialogue states; and (ii) the investigation of novel modelling approaches to capture slot dependencies in dialogue domains. The analysis of language\u27s structural properties was conducted with a corpus-based study to investigate whether variable dependencies, i.e., slot dependencies when using dialogue system terminology, exist in dialogue domains, and if yes, to what extent do these dependencies affect the dialogue state tracking process. A number of public dialogue corpora were chosen for analysis with a collection of statistical methods being applied to their analysis. Deep learning architectures have been shown in various works to be an effective method to model conversations and different types of machine learning challenges. In this research, in order to account for slot dependencies, a number of deep learning-based models were experimented with for the dialogue state tracking task. In particular, a multi-task learning system was developed to study the leveraging of common features and shared knowledge in the training of dialogue state tracking subtasks such as tracking different slots, hence investigating the associations between these slots. Beyond that, a structured prediction method, based on energy-based learning, was also applied to account for explicit dialogue slot dependencies. The study results show promising directions for solving the dialogue state tracking challenge for task-oriented dialogue systems. By accounting for slot dependencies in dialogue domains, dialogue states were produced more accurately when benchmarked against comparative modelling methods that do not take advantage of the same principle. Furthermore, the structured prediction method is applicable to various state-of-the-art modelling approaches for further study. In the long term, the study of dialogue state slot dependencies can potentially be expanded to a wider range of conversational aspects such as personality, preferences, and modalities, as well as user intents

Random Access Schemes in Wireless Systems With Correlated User Activity

Traditional random access schemes are designed based on the aggregate process of user activation, which is created on the basis of independent activations of the users. However, in Machine-Type Communications (MTC), some users are likely to exhibit a high degree of correlation, e.g. because they observe the same physical phenomenon. This paves the way to devise access schemes that combine scheduling and random access, which is the topic of this work. The underlying idea is to schedule highly correlated users in such a way that their transmissions are less likely to result in a collision. To this end, we propose two greedy allocation algorithms. Both attempt to maximize the throughput using only pairwise correlations, but they rely on different assumptions about the higher-order dependencies. We show that both algorithms achieve higher throughput compared to the traditional random access schemes, suggesting that user correlation can be utilized effectively in access protocols for MTC.Comment: Submitted to SPAWC 201