Gabriella Pasi


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DoSSIER at MedVidQA 2022: Text-based Approaches to Medical Video Answer Localization Problem
Wojciech Kusa | Georgios Peikos | Óscar Espitia | Allan Hanbury | Gabriella Pasi
Proceedings of the 21st Workshop on Biomedical Language Processing

This paper describes our contribution to the Answer Localization track of the MedVidQA 2022 Shared Task. We propose two answer localization approaches that use only textual information extracted from the video. In particular, our approaches exploit the text extracted from the video’s transcripts along with the text displayed in the video’s frames to create a set of features. Having created a set of features that represents a video’s textual information, we employ four different models to measure the similarity between a video’s segment and a corresponding question. Then, we employ two different methods to obtain the start and end times of the identified answer. One of them is based on a random forest regressor, whereas the other one uses an unsupervised peak detection model to detect the answer’s start time. Our findings suggest that for this task, leveraging only text-related features (transmitted either verbally or visually) and using a small amount of training data, lead to significant improvements over the benchmark Video Span Localization model that is based on deep neural networks.


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IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages
Rexhina Blloshmi | Tommaso Pasini | Niccolò Campolungo | Somnath Banerjee | Roberto Navigli | Gabriella Pasi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably. However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) performing neural ranking on non-English documents is still cumbersome due to shortage of labeled datasets. In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information. At the core of our approach lies a novel multilingual query expansion mechanism based on Word Sense Disambiguation that provides sense definitions as additional semantic information for the query. Importantly, we use senses as a bridge across languages, thus allowing our model to perform considerably better than its supervised and unsupervised alternatives across French, German, Italian and Spanish languages on several CLEF benchmarks, while being trained on English Robust04 data only. We release SIR at