An Empirical Examination of the Moderators of the Service Recovery Paradox

Some researchers (Abrams and Paese, 1993; Bitner, Booms, and Tetreault, 1990; Etzel and Silverman. 1981; Feinberg et al., 1990; Folkes and Kotsos, 1986; Gilly and Gelb, 1982; Hart, Heskett, and Sasser, 1990; Hocutt, Chakraborty, and Mowen, 1997; Kelley and Davis, 1994; Kelley, Hoffman and Davis, 1993; McCollough and Bharadwaj, 1992; Michel, 2001; Chrage, 2001; Smith and Bolton, 1998; Spreng, Harrell, and Mackoy, 1995; Tax, Brown, and Chandrashekaran, 1998) support the notion of a ‘recovery paradox’ which states that the occurrence of a failure may, if the recovery is effective, offer an opportunity to acquire higher satisfaction ratings from customers than if the failure had never happened. While a number of researchers have provided evidence in support of the recovery paradox, several recent studies (Andreassen, 2001; Maxham, 2001; Maxham and Netemeyer, 2002; McCollough et al. 2000) have failed to find such support. This dissertation theoretically and empirically examines factors which moderate the occurrence of a ‘recovery paradox’ in the event of a service failure. The research findings indicate that, under appropriate conditions, a customer can experience a paradoxical satisfaction increase after a service failure. One such condition entails the severity of the failure. That is, results indicate that it is unlikely that a first-rate redress initiative can return the satisfaction of a severe failure recipient to par. The findings of this investigation also reveal that a customer who has experienced a prior failure with the firm is less likely to be impressed by a superb recovery than a customer who has never encountered a problem with the service provider. In addition, customers are more forgiving of failures that occur during a process than mistakes that occur as part of the outcome. Furthermore, both control and stability intervene to affect the likelihood of increases in post-failure customer satisfaction. That is, people are more forgiving if they feel that the failure was not reasonably foreseeable to the service provider. Likewise, customers are more apt to exonerate the firm if they assess that the failure is unlikely to happen again. Lastly, this research found that control and relationship type interact to influence the probability of a recovery paradox. Specifically, customers in a true relationship are more likely to accept a low control explanation of the failure than customers in a pseudo-relationship with the firm

Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification

Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domains are frequently added, creating sizeable data is expensive. We show that lightweight augmentation, a set of augmentation methods involving word span and sentence level operations, alleviates data scarcity problems. Our experiments on limited data settings show that lightweight augmentation yields significant performance improvement on slot filling on the ATIS and SNIPS datasets, and achieves competitive performance with respect to more complex, state-of-the-art, augmentation approaches. Furthermore, lightweight augmentation is also beneficial when combined with pre-trained LM-based models, as it improves BERT-based joint intent and slot filling models.Comment: Accepted at PACLIC 2020 - The 34th Pacific Asia Conference on Language, Information and Computatio

Simulating Domain Changes in Conversational Agents Through Dialogue Adaptation

A major bottleneck for the large diffusion of data-driven conversational agents is that conversational domains are subject to continuous changes, which soon make initial dialogue models inadequate to manage new situations. In the current context, updating training data is usually carried on manually, and, in addition, there are no tools for simulating the impact of a certain domain change on the performance of the dialogue system. This position paper advocates that substantial progress in the capacity to simulate domain changes is based on the ability to automatically adapt training and test dialogues to those changes. We discuss the potential of a simulation framework for task-oriented dialogues, as well as the research challenges that need to be addressed

Object-based predictive modeling (OBPM) for archaeology: finding control places in mountainous environments

none2This contribution examines the potential of object-based image analysis (OBIA) for archaeological predictive modeling starting from elevation data, by testing a ruleset for the location of “control places” on two test areas in the Alpine environment (northern Italy). The ruleset was developed on the western Asiago Plateau (Vicenza Province, Veneto) and subsequently re-applied (semi)automatically in the Isarco Valley (South Tirol). Firstly, we considered the physiographic, climatic, and morphological characteristics of the selected areas and we applied 3 DTM processing techniques: Slope, local dominance, and solar radiation. Subsequently, we employed an object-based approach to classification. Solar radiation, local dominance, and slope were visualized as a three-layer RGB image that was segmented with the multiresolution algorithm. The classification was implemented with a ruleset that selected only image–objects with high local dominance and solar radiation, but low slope, which were considered more suitable parameters for human occupation. The classification returned five areas on the Asiago Plateau that were remotely and ground controlled, confirming anthropic exploitation covering a time span from protohistory (2nd-1st millennium BC) to the First World War. Subsequently, the same model was applied to the Isarco Valley to verify the replicability of the method. The procedure resulted in 36 potential control places which find good correspondence with the archaeological sites discovered in the area. Previously unknown contexts were further controlled using very high-resolution (VHR) aerial images and digital terrain model (DTM) data, which often suggested a possible (pre-proto)historic human frequentation. The outcomes of the analysis proved the feasibility of the approach, which can be exported and applied to similar mountainous landscapes for site predictivity analysis.openMagnini, Luigi; Bettineschi, CinziaMagnini, Luigi; Bettineschi, Cinzi

Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems

In task-oriented dialogue systems the dialogue state tracker (DST) component is responsible for predicting the state of the dialogue based on the dialogue history. Current DST approaches rely on a predefined domain ontology, a fact that limits their effective usage for large scale conversational agents, where the DST constantly needs to be interfaced with ever-increasing services and APIs. Focused towards overcoming this drawback, we propose a domain-aware dialogue state tracker, that is completely data-driven and it is modeled to predict for dynamic service schemas. The proposed model utilizes domain and slot information to extract both domain and slot specific representations for a given dialogue, and then uses such representations to predict the values of the corresponding slot. Integrating this mechanism with a pretrained language model (i.e. BERT), our approach can effectively learn semantic relations

A Robust Data-Driven Approach for Dialogue State Tracking of Unseen Slot Values

A Dialogue State Tracker is a key component in dialogue systems which estimates the beliefs of possible user goals at each dialogue turn. Deep learning approaches using recurrent neural networks have shown state-of-the-art performance for the task of dialogue state tracking. Generally, these approaches assume a predefined candidate list and struggle to predict any new dialogue state values that are not seen during training. This makes extending the candidate list for a slot without model retaining infeasible and also has limitations in modelling for low resource domains where training data for slot values are expensive. In this paper, we propose a novel dialogue state tracker based on copying mechanism that can effectively track such unseen slot values without compromising performance on slot values seen during training. The proposed model is also flexible in extending the candidate list without requiring any retraining or change in the model. We evaluate the proposed model on various benchmark datasets (DSTC2, DSTC3 and WoZ2.0) and show that our approach, outperform other end-to-end data-driven approaches in tracking unseen slot values and also provides significant advantages in modelling for DST

Scalable Neural Dialogue State Tracking

A Dialogue State Tracker (DST) is a key component in a dialogue system aiming at estimating the beliefs of possible user goals at each dialogue turn. Most of the current DST trackers make use of recurrent neural networks and are based on complex architectures that manage several aspects of a dialogue, including the user utterance, the system actions, and the slot-value pairs defined in a domain ontology. However, the complexity of such neural architectures incurs into a considerable latency in the dialogue state prediction, which limits the deployments of the models in real-world applications, particularly when task scalability (i.e. amount of slots) is a crucial factor. In this paper, we propose an innovative neural model for dialogue state tracking, named Global encoder and Slot-Attentive decoders (G-SAT), which can predict the dialogue state with a very low latency time, while maintaining high-level performance. We report experiments on three different languages (English, Italian, and German) of the WoZ2.0 dataset, and show that the proposed approach provides competitive advantages over state-of-art DST systems, both in terms of accuracy and in terms of time complexity for predictions, being over 15 times faster than the other systems.Comment: 8 pages, 3 figures, Accepted at ASRU 201

Image processing and analysis of radar and lidar data: new discoveries in Verona southern lowland (Italy)

ABSTRACTThis contribution proposes an evaluation of lidar and radar data processing and its potential in revealing archaeological features within a level plain environment, the southern lowland of Verona (Italy), focusing on evidences dating back to the Bronze Age. Many archaeological sites in the research area, including some of the most outstanding settlements of Terramare Culture, were identified or at least examined through aerial photo observation. Even if in several occasions modern agricultural activities contributed to the discoveries, bringing to the surface artifacts and scrapes of buried layers, this kind of impact has also been progressively deteriorating the archaeological record, hence the proto-historic landscape is now discernible through evanescent marks which cannot be always detected using customary optical sensors. Lidar and radar data analysis has then been considered as an alternative, non-invasive method of investigation on such a vast area