Weakly-Supervised Neural Response Selection from an Ensemble of Task-Specialised Dialogue Agents

Dialogue engines that incorporate different types of agents to converse with humans are popular. However, conversations are dynamic in the sense that a selected response will change the conversation on-the-fly, influencing the subsequent utterances in the conversation, which makes the response selection a challenging problem. We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents by taking into account the conversational history, and propose a \emph{Neural Response Selection} method. The proposed method is trained to predict a coherent set of responses within a single conversation, considering its own predictions via a curriculum training mechanism. Our experimental results show that the proposed method can accurately select the most appropriate responses, thereby significantly improving the user experience in dialogue systems

Incremental Semantic Evaluation for Interactive Systems: Inertia, Pre-emption, and Relations

Although schemes for incremental semantic evaluation have been explored and refined for more than two decades, the demands of user interaction continue to outstrip the capabilities of these schemes. The feedback produced by a semantic evaluator must support the user's programming activities: it must be structured in a way that provides the user with meaningful insight into the program (directly, or via other tools in the environment) and it must be timely. In this paper we extend an incremental attribute evaluation scheme with three techniques to better meet these demands within the context of a modeless editing system with a flexible tool integration paradigm. Efficient evaluation in the presence of syntax errors (which arise often under modeless editing) is supported by giving semantic attributes inertia: a tendency to not change unless necessary. Pre-emptive evaluation helps to reduce the delays associated with a sequence of edits, allowing an evaluator to "keep pace" with the user. Relations provide a general means to capture semantic structure (for the user, other tools, and as attributes within an evaluation) and are treated efficiently using a form of differential propagation. The combination of these three techniques meets the demands of user interaction; leaving out any one does not

The assessment of usability of electronic shopping: A heuristic evaluation

Today there are thousands of electronic shops accessible via the Web. Some provide user-friendly features whilst others seem not to consider usability factors at all. Yet, it is critical that the electronic shopping interface is user-friendly so as to help users to obtain their desired results. This study applied heuristic evaluation to examine the usability of current electronic shopping. In particular, it focused on four UK-based supermarkets offering electronic services: including ASDA, Iceland, Sainsbury, and Tesco. The evaluation consists of two stages: a free-flow inspection and a task-based inspection. The results indicate that the most significant and common usability problems have been found to lie within the areas of ‘User Control and Freedom’ and ‘Help and Documentation’. The findings of this study are applied to develop a set of usability guidelines to support the future design of effective interfaces for electronic shopping

S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction

The technology of hyperspectral imaging (HSI) records the visual information upon long-range-distributed spectral wavelengths. A representative hyperspectral image acquisition procedure conducts a 3D-to-2D encoding by the coded aperture snapshot spectral imager (CASSI) and requires a software decoder for the 3D signal reconstruction. By observing this physical encoding procedure, two major challenges stand in the way of a high-fidelity reconstruction. (i) To obtain 2D measurements, CASSI dislocates multiple channels by disperser-titling and squeezes them onto the same spatial region, yielding an entangled data loss. (ii) The physical coded aperture leads to a masked data loss by selectively blocking the pixel-wise light exposure. To tackle these challenges, we propose a spatial-spectral (S^2-) Transformer network with a mask-aware learning strategy. First, we simultaneously leverage spatial and spectral attention modeling to disentangle the blended information in the 2D measurement along both two dimensions. A series of Transformer structures are systematically designed to fully investigate the spatial and spectral informative properties of the hyperspectral data. Second, the masked pixels will induce higher prediction difficulty and should be treated differently from unmasked ones. Thereby, we adaptively prioritize the loss penalty attributing to the mask structure by inferring the pixel-wise reconstruction difficulty upon the mask-encoded prediction. We theoretically discusses the distinct convergence tendencies between masked/unmasked regions of the proposed learning strategy. Extensive experiments demonstrates that the proposed method achieves superior reconstruction performance. Additionally, we empirically elaborate the behaviour of spatial and spectral attentions under the proposed architecture, and comprehensively examine the impact of the mask-aware learning.Comment: 11 pages, 16 figures, 6 tables, Code: https://github.com/Jiamian-Wang/S2-transformer-HS