Open-domain conversational QA (ODCQA) calls for effective question rewriting (QR), as the questions in a conversation typically lack proper context for the QA model to interpret. In this paper, we compare two types of QR approaches, generative and expansive QR, in end-to-end ODCQA systems with recently released QReCC and OR-QuAC benchmarks. While it is common practice to apply the same QR approach for both the retriever and the reader in the QA system, our results show such strategy is generally suboptimal and suggest expansive QR is better for the sparse retriever and generative QR is better for the reader. Furthermore, while conversation history modeling with dense representations outperforms QR, we show the advantages to apply both jointly, as QR boosts the performance especially when limited history turns are considered.

In this paper, we present a framework for performing automatic analysis of Land Use Zones based on Location-Based Social Networks (LBSNs). We model city areas using a hierarchical structure of POIs extracted from foursquare. We encode such structures in kernel machines, e.g., Support Vector Machines, using a new Tree Kernel, i.e., the Hierarchical POI Kernel (HPK), which can take the importance of the individual POIs into account during the substructure matching. This way, HPK projects structures in the space of all their possible substructures such that each dimension corresponds to a semantic structural feature, weighted according to the discriminative power of POIs . We generated four different datasets for the following cities: Barcelona, Lisbon, Amsterdam and Milan, where we trained and tested our models. The results show that our approach largely outperforms previous work and standard baseline built on simple features, such as counts of different POIs. Finally, we apply a mining algorithm to extract the most relevant features (tree fragments) from the implicit TK space according to the weights the kernel machine assigned to them. Our approach can produce an explicit set of representative features that can be used to classify and characterize urban areas.

In this paper, we study how to model taxi drivers' behavior and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well-studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behavior and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, the RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset-based on the city of Porto-, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.

The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g., floods, terrorist attacks, etc. . . ). In this perspective, we propose ACHILLES, an application to models people's movements in a given transport mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.

In this paper, we study the impact of relational and syntactic representations for an interesting and challenging task: the automatic resolution of crossword puzzles.Automatic solvers are typically based on two answer retrieval modules: (i) a web search engine, e.g., Google, Bing, etc. and (ii) a database (DB) system for accessing previously resolved crossword puzzles.We show that learning to rank models based on relational syntactic structures defined between the clues and the answer can improve both modules above.In particular, our approach accesses the DB using a search engine and reranks its output by modeling paraphrasing.This improves on the MRR of previous system up to 53% in ranking answer candidates and greatly impacts on the resolution accuracy of crossword puzzles up to 15%.