Improving Retrieval-Based Question Answering with Deep Inference Models

Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex science questions which require some form of inference to determine the correct answer. In this paper, we present a two-step method that combines information retrieval techniques optimized for question answering with deep learning models for natural language inference in order to tackle the multi-choice question answering in the science domain. For each question-answer pair, we use standard retrieval-based models to find relevant candidate contexts and decompose the main problem into two different sub-problems. First, assign correctness scores for each candidate answer based on the context using retrieval models from Lucene. Second, we use deep learning architectures to compute if a candidate answer can be inferred from some well-chosen context consisting of sentences retrieved from the knowledge base. In the end, all these solvers are combined using a simple neural network to predict the correct answer. This proposed two-step model outperforms the best retrieval-based solver by over 3% in absolute accuracy.Comment: 8 pages, 2 figures, 8 tables, accepted at IJCNN 201

Applicability of the technology acceptance model for widget-based personal learning environments

This contribution presents results from two exploratory studies on technology acceptance and use of widget-based personal learning environments. Methodologically, the investigation carried out applies the unified theory of acceptance and use of technology (UTAUT). With the help of this instrument, the study assesses expert judgments about intentions to use and actual use of the emerging technology of flexibly arranged combinations of use-case-sized mini learning tools. This study aims to explore the applicability of the UTAUT model and questionnaire for widget-based personal learning environments and reports back on the experiences gained with the two studies

Explaining Vision and Language through Graphs of Events in Space and Time

Artificial Intelligence makes great advances today and starts to bridge the gap between vision and language. However, we are still far from understanding, explaining and controlling explicitly the visual content from a linguistic perspective, because we still lack a common explainable representation between the two domains. In this work we come to address this limitation and propose the Graph of Events in Space and Time (GEST), by which we can represent, create and explain, both visual and linguistic stories. We provide a theoretical justification of our model and an experimental validation, which proves that GEST can bring a solid complementary value along powerful deep learning models. In particular, GEST can help improve at the content-level the generation of videos from text, by being easily incorporated into our novel video generation engine. Additionally, by using efficient graph matching techniques, the GEST graphs can also improve the comparisons between texts at the semantic level.Comment: Accepted at IEEE International Conference on Computer Vision (ICCV) 2023 Workshops: 5th Workshop On Closing The Loop Between Vision And Languag

Analiza stărilor emoționale induse de citirea unei știri utilizând Analiza Semantică Latentă

International audienceEmoţiile pot fi identificate atât în comunicarea verbală cât şi în cea scrisă. Dacă în primul caz pot fi identificate mai ușor datorită unor trăsături specifice comunicării verbale (limbajul corpului, tonul vocii sau inflexiuni), în al doilea caz regăsirea acestora poate fi o adevărată provocare. Aşadar, propunem o metodă inedită de analiză automată a emoţiilor transmise prin intermediul comunicării scrise, mai exact, determinarea stării emoţionale a unei persoane în urma citirii unei ştiri. Cu alte cuvinte, scopul nostru este de a determina cum citirea unei ştiri afectează starea emoţională a cititorului şi să ajustăm aceste valori pe baza stării emoţionale curente a acestuia. Dintr-o perspectivă mai tehnică, sistemul dezvoltat (Emo2 – Emotions Monitor) combină o abordare independentă de context (evaluarea efectivă a ştirii utilizând tehnici de prelucrare a limbajului natural) cu influenţele determinate de starea emoţională curentă a utilizatorului. Astfel, scopul metodei propuse este de a obţine o estimare a stării emoţionale finale a utilizatorului cât mai apropiată de cea reală

QAnswer -Enhanced Entity Matching for Question Answering over Linked Data

Abstract. QAnswer is a question answering system that uses DBpedia as a knowledge base and converts natural language questions into a SPARQL query. In order to improve the match between entities and relations and natural language text, we make use of Wikipedia to extract lexicalizations of the DBpedia entities and then match them with the question. These entities are validated on the ontology, while missing ones can be inferred. The proposed system was tested in the QALD-5 challenge and it obtained a F1 score of 0.30, which placed QAnswer in the second position in the challenge, despite the fact that the system used only a small subset of the properties in DBpedia, due to the long extraction process