Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms

Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the increasing multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain, and adapt the underlying concept and architecture for automated category classification and expert retrieval on image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of Yahoo! Answers. To the best of our knowledge, this is the first work to tackle the multimodality challenge in CQA, and to adapt VQA models for tasks on a more ecologically valid source of visual questions. Our analysis of the differences between visual QA and community QA data drives our proposal of novel augmentations of an attention method tailored for CQA, and use of auxiliary tasks for learning better grounding features. Our final model markedly outperforms the text-only and VQA model baselines for both tasks of classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201

Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatiotemporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”

Translating Video Recordings of Mobile App Usages into Replayable Scenarios

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers. Unfortunately, automatically analyzing screen recordings presents serious challenges, due to their graphical nature, compared to other types of (textual) artifacts. To address these challenges, this paper introduces V2S, a lightweight, automated approach for translating video recordings of Android app usages into replayable scenarios. V2S is based primarily on computer vision techniques and adapts recent solutions for object detection and image classification to detect and classify user actions captured in a video, and convert these into a replayable test scenario. We performed an extensive evaluation of V2S involving 175 videos depicting 3,534 GUI-based actions collected from users exercising features and reproducing bugs from over 80 popular Android apps. Our results illustrate that V2S can accurately replay scenarios from screen recordings, and is capable of reproducing $\approx$ 89% of our collected videos with minimal overhead. A case study with three industrial partners illustrates the potential usefulness of V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software Engineering (ICSE'20), 13 page

On the integration of conceptual hierarchies with deep learning for explainable open-domain question answering

Question Answering, with its potential to make human-computer interactions more intuitive, has had a revival in recent years with the influx of deep learning methods into natural language processing and the simultaneous adoption of personal assistants such as Siri, Google Now, and Alexa. Unfortunately, Question Classification, an essential element of question answering, which classifies questions based on the class of the expected answer had been overlooked. Although the task of question classification was explicitly developed for use in question answering systems, the more advanced task of question classification, which classifies questions into between fifty and a hundred question classes, had developed into independent tasks with no application in question answering. The work presented in this thesis bridges this gap by making use of fine-grained question classification for answer selection, arguably the most challenging subtask of question answering, and hence the defacto standard of measure of its performance on question answering. The use of question classification in a downstream task required significant improvement to question classification, which was achieved in this work by integrating linguistic information and deep learning through what we call Types, a novel method of representing Concepts. Our work on a purely rule-based system for fine-grained Question Classification using Types achieved an accuracy of 97.2%, close to a 6 point improvement over the previous state of the art and has remained state of the art in question classification for over two years. The integration of these question classes and a deep learning model for Answer Selection resulted in MRR and MAP scores which outperform the current state of the art by between 3 and 5 points on both versions of a standard test set