Text-to-Vis is an emerging task in the data engineering and mining area that aims to automatically generate data visualizations from natural language questions (NLQs). Despite their progress, existing text-to-vis models often heavily rely on lexical matching between words in the questions and tokens in data schemas. This overreliance on lexical matching may lead to a diminished level of model robustness against input variations. In this study, we thoroughly examine the robustness of current text-to-vis models, an area that has not previously been explored. In particular, we construct the first robustness dataset nvBench-Rob, which contains diverse lexical and phrasal variations based on the original text-to-vis benchmark nvBench. Then, we found that the performance of existing text-to-vis models on this new dataset dramatically drops, implying that these methods exhibit inadequate robustness overall. Finally, we propose a novel framework based on Retrieval-Augmented Generation (RAG) technique, named GRED, specifically designed to address input perturbations in these two variants. The framework consists of three parts: NLQ-Retrieval Generator, Visualization Query-Retrieval Retuner and Annotation-based Debugger, which are used to tackle the challenges posed by natural language variants, programming style differences and data schema variants, respectively. Extensive experimental evaluations show that, compared to the state-of-the-art model Prompt4Vis in the Text-to-Vis field, GRED performs better in terms of model robustness, with a 40% increase in accuracy on the proposed nvBench-Rob dataset.

Given a massive dataset, data visualization (DV) could efficiently express the insights and summaries behind the massive raw data by employing vivid visual representations. To create suitable DVs, users are required to get a comprehensive understanding of the raw data and then transfer their ideas into DVs by composing a suitable and accurate specification in some declarative visualization languages (DVLs, e.g., Vega-Lite). A specification is a JSON object defining the properties of the DVs, like the selected data, the transformations, the visual details, and so on. Due to its complicated grammar and details, DV has quite a steep learning curve, even for data analysts. In this paper, we propose a new task named FeVisQA, referring to Free-form Question Answering over data Visualizations. More specifically,-given a raw dataset, a related DV (in the form of a specification), and a question, FeVisQA aims to predict a textual answer automatically. As a particular case of the general CodeQA (i.e., QA over general programming code like Python and Java) task, FeVisQA enables people to better comprehend data and its DVs by conducting logical reasoning when answering these questions. Since FeVisQA has not been studied in the literature, we first construct a benchmark dataset containing 152 datasets, 14,406 DVs, and 83,890 QA pairs. To tackle this new task, we design a novel neural network named FeVisQANet with advanced multi-modal encoder and adaptive decoder structures, and we also design a novel multi-step framework called VisQA for Multi-modal Large Language Models (MLLMs) based on Retrieval-augmented Generation (RAG) technology. Extensive experiments on our constructed datasets validate the rationale and effectiveness of this proposed FeVisQA task and the proposed model. While research on QA over text and table, machine reading comprehension, and CodeQA develops rapidly, prior works have yet to draw attention to question-answering over DVs. This study connects two important subareas, QA from the natural language process area and DV from the data engineering area. We hope this new dataset and model can serve as a helpful benchmark that would benefit the development of both fields.

Question Answering (QA) systems playa vital role in knowledge acquisition. CodeQA refers to question answering (QA) over source code for code comprehension purpose. However, existing CodeQA studies mainly focus on questions related to general-purpose programming languages (GPLs) (e.g., Java and Python), and no study has been conducted on QA over declarative visualization languages (DVLs) (e.g., Vega-Lite), a kind of programming languages used for creating data visualization (DV). DVLs enjoys specific grammars that are instinct different from GPLs. This demonstration presents the first neural-based QA system for DVL, FeVisQASystem. FeVisQASystem is based on a new task named Fevisqa, short for Free-form QA over data Visualization, which takes natural language questions and DV specification as inputs to predict the answers to the questions. As a particular case of the CodeQA task, Fe VisQA enables people to better comprehend data and its DV s by conducting logical reasoning when answering these questions. Although research on question-answering and machine reading comprehension is progressing quickly, little attention has previously been paid to FeVisQA. This new system and the task can serve as a helpful pioneering study for DV comprehension. The video can be accessed via https://ldrv.ms/f/s!Ah2vhboIPBFMhk6jTYOtaIRnLC2K?e=OkJqOq

Stack Overflow is a widely-used community Q&A website for programming-related queries.In such a platform, providing related questions as suggestions to the users can significantly enhance their search experience.Although there are many approaches based on deep learning that can automatically predict the relatedness between questions, those approaches are limited because the semantic and interaction features of the sentences may be lost.In this paper, we propose a novel method to predict the relatedness between questions based on semantic matching.We adopt the Interaction Feature Extractor to capture the interaction information and fuse it through a fusion mechanism to enhance the interaction between questions.Our experimental results demonstrate that our proposed method achieves stateof-the-art performance in terms of Precision, Recall, and F1score evaluation metrics, outperforming the baseline approaches.Furthermore, we show that our model also performs well in other semantic matching tasks in software fields, indicating its generalization ability and robustness.

As one of the most popular programming forums, Stack Overflow has helped many developers with massive high-quality questions and answers. Particularly, the related questions identified by developers can supply targeted knowledge to solve the programming problems. However, it is difficult to identify all relevant questions by developers from massive questions in Stack Overflow. Although some studies have raised methods for automatically identifying relatedness between questions, only a few of them provided related questions to new query. In addition, the existing methods can not extract the global information between query and candidate questions in a proper way. In this paper, we propose a novel method that recommends the related questions to developers' new queries based on the semantic matching. We introduce a novel integral fusion to improve the global information extraction and use the inter-attention to capture the local interactive information. Besides, we have pre-trained domain-specific word embeddings to enhance the processing of software engineering information. The experiment results show that our model achieves competitive performance in MRR, nDCG@5, and nDCG@10 metrics in the related questions retrieval on Stack Overflow,

Due to the highly competitive and dynamic mobile application (app) market, app developers need to release new versions regularly to improve existing features and provide new features for users.To accomplish the maintenance and evolution tasks more effectively and efficiently, app developers should collect and analyze user reviews, which contain a rich source of information from user perspective.Although there are many approaches based on intention mining that can automatically predict the intention of reviews for better understanding valuable information, those approaches are limited since contextual information of the whole review text may be lost.In this paper, we propose Mining Intention from App Reviews (MIAR), a novel deep learning model to predict the intention of app reviews automatically.We adopt a Contextual Feature Extractor to capture the context semantic information and fuse it with the local feature through a fusion mechanism.The experiment results demonstrate that MIAR has made significant improvement over the baseline approaches in Precision, Recall, and F1-score evaluation metrics, achieving state-of-the-art performance in this task.Our model also performs well in other intention mining tasks, proving its generalization ability and robustness.

Due to the highly competitive and dynamic mobile application (app) market, app developers need to release new versions regularly to improve existing features and provide new features for users. To accomplish the maintenance and evolution tasks more effectively and efficiently, app developers should collect and analyze user reviews, which contain a rich source of information from user perspective. Although there are many approaches based on intention mining that can automatically predict the intention of reviews for better understanding valuable information, those approaches are limited since contextual information of the whole review text may be lost. In this paper, we propose Mining Intention from App Reviews (MIAR), a novel deep learning model to predict the intention of app reviews automatically. We adopt a Contextual Feature Extractor to capture the context semantic information and fuse it with the local feature through a fusion mechanism. The experiment results demonstrate that MIAR has made significant improvement over the baseline approaches in Precision, Recall and [Formula: see text]-score evaluation metrics, achieving state-of-the-art performance in this task. Our model also performs well in other intention mining tasks, proving its generalization ability and robustness.