How big is the test set used for evaluating the proposed re-ranking approach?
3084 real user requests  assigned to suitable answers from the training corpus.
We implement a method for re-ranking top-10 results of a state-of-the-art question answering (QA) system. The goal of our re-ranking approach is to improve the answer selection given the user question and the top-10 candidates. We focus on improving deployed QA systems that do not allow re-training or re-training comes at a high cost. Our re-ranking approach learns a similarity function using n-gram based features using the query, the answer and the initial system confidence as input. Our contributions are: (1) we generate a QA training corpus starting from 877 answers from the customer care domain of T-Mobile Austria, (2) we implement a state-of-the-art QA pipeline using neural sentence embeddings that encode queries in the same space than the answer index, and (3) we evaluate the QA pipeline and our re-ranking approach using a separately provided test set. The test set can be considered to be available after deployment of the system, e.g., based on feedback of users. Our results show that the system performance, in terms of top-n accuracy and the mean reciprocal rank, benefits from re-ranking using gradient boosted regression trees. On average, the mean reciprocal rank improves by 9.15%.
In this work, we examine the problem of incrementally improving deployed QA systems in an industrial setting. We consider the domain of customer care of a wireless network provider and focus on answering frequent questions (focussing on the long tail of the question distribution BIBREF0 ). In this setting, the most frequent topics are covered by a separate industry-standard chatbot based on hand-crafted rules by dialogue engineers. Our proposed process is based on the augmented cross-industry standard process for data mining BIBREF1 (augmented CRISP data mining cycle). In particular, we are interested in methods for improving a model after its deployment through re-ranking of the initial ranking results. In advance, we follow the steps of the CRISP cycle towards deployment for generating a state-of-the-art baseline QA model. First, we examine existing data (data understanding) and prepare a corpus for training (data preparation). Second, we implement and train a QA pipeline using state-of-the-art open source components (modelling). We perform an evaluation using different amounts of data and different pipeline configurations (evaluation), also to understand the nature of the data and the application (business understanding). Third, we investigate the effectiveness and efficiency of re-ranking in improving our QA pipeline after the deployment phase of CRISP. Adaptivity after deployment is modelled as (automatic) operationalisation step with external reflection based on, e.g., user feedback. This could be replaced by introspective meta-models that allow the system to enhance itself by metacognition BIBREF1 . The QA system and the re-ranking approach are evaluated using a separate test set that maps actual user queries from a chat-log to answers of the QA corpus. Sample queries from the evaluation set with one correct and one incorrect sample are shown in Table TABREF1 . With this work, we want to answer the question whether a deployed QA system that is difficult to adapt and that provides a top-10 ranking of answer candidates, can be improved by an additional re-ranking step that corresponds to the operationalisation step of the augmented CRISP cycle. It is also important to know the potential gain and the limitations of such a method that works on top of an existing system. We hypothesise that our proposed re-ranking approach can effectively improve ranking-based QA systems.