Search Results for author:
Found 17 papers, 11 papers with code
Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot
Creating and deploying customized applications is crucial for operational success and enriching user experiences in the rapidly evolving modern business world.
i-Align: an interpretable knowledge graph alignment model
One of the strategies to address this problem is KG alignment, i. e., forming a more complete KG by merging two or more KGs.
Designing and Evaluating Presentation Strategies for Fact-Checked Content
We address this research gap by exploring the critical design elements in fact-checking reports and investigating whether credibility and presentation-based design improvements can enhance users' ability to interpret the report accurately.
Mitigating Negative Transfer with Task Awareness for Sexism, Hate Speech, and Toxic Language Detection
Therefore another solution, based on the sharing of information between tasks, has been developed: Multi-Task Learning (MTL).
AI-UPV at EXIST 2023 -- Sexism Characterization Using Large Language Models Under The Learning with Disagreements Regime
In particular, our system is articulated in three different pipelines.
Examining the Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing Activities
Physiological signals can potentially be applied as objective measures to understand the behavior and engagement of users interacting with information access systems.
ITTC @ TREC 2021 Clinical Trials Track
This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the TREC 2021 Clinical Trials Track.
Evaluating Fairness in Argument Retrieval
In this work, we analyze a range of non-stochastic fairness-aware ranking and diversity metrics to evaluate the extent to which argument stances are fairly exposed in argument retrieval systems.
The Many Dimensions of Truthfulness: Crowdsourcing Misinformation Assessments on a Multidimensional Scale
We deploy a set of quality control mechanisms to ensure that the thousands of assessments collected on 180 publicly available fact-checked statements distributed over two datasets are of adequate quality, including a custom search engine used by the crowd workers to find web pages supporting their truthfulness assessments.
Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19
Our results show that: workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e. g., source of information, behavior) impact the quality of the data.
The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?
Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large.
Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background
Truthfulness judgments are a fundamental step in the process of fighting misinformation, as they are crucial to train and evaluate classifiers that automatically distinguish true and false statements.
Towards a Model for Spoken Conversational Search
Conversation is the natural mode for information exchange in daily life, a spoken conversational interaction for search input and output is a logical format for information seeking.
Informing the Design of Spoken Conversational Search
We compared the interactions to existing models of search behaviour.
Prosody Modifications for Question-Answering in Voice-Only Settings
Many popular form factors of digital assistants---such as Amazon Echo, Apple Homepod, or Google Home---enable the user to hold a conversation with these systems based only on the speech modality.
Active Learning for Entity Filtering in Microblog Streams
Monitoring the reputation of entities such as companies or brands in microblog streams (e. g., Twitter) starts by selecting mentions that are related to the entity of interest.
Real-Time Classification of Twitter Trends
Social media users give rise to social trends as they share about common interests, which can be triggered by different reasons.
