Papers with question-answering system
Generating Vehicular Icon Descriptions and Indications Using Large Vision-Language Models (2024.emnlp-industry)
Copied to clipboard
James Fletcher, Nicholas Dehnen, Seyed Nima Tayarani Bathaie, Aijun An, Heidar Davoudi, Ron DiCarlantonio, Gary Farmaner
| Challenge: | Existing image description systems are trained mainly on natural images, whereas icon images are drawings. |
| Approach: | They propose to use a dataset to generate both visual and functional icon descriptions based on the icon image and its context information in the car manual. |
| Outcome: | The proposed model performs well on the dashboard icon description task while the third model perform poorly. |
A Virtual Patient Dialogue System Based on Question-Answering on Clinical Records (2024.lrec-main)
Copied to clipboard
| Challenge: | a new approach to annotating medical dialogues with intents is proposed for virtual patients . a VP is a system that allows medical students to simulate a real clinical consultation . |
| Approach: | They propose to annotate medical dialogue questions in Spanish and a second dataset of dialogues using a novel annotation approach. |
| Outcome: | The proposed approach eliminates the need for manually structured patient records . the two datasets and the code will be freely available for the research community. |
Cross-Task Generalization via Natural Language Crowdsourcing Instructions (2022.acl-long)
Copied to clipboard
| Challenge: | Despite the success of supervised learning, models often struggle with generalization across tasks. |
| Approach: | They propose to use crowdsourcing instructions to build a model that learns a new task by understanding the human-readable instructions that define it. |
| Outcome: | The proposed model can learn from seen tasks and generalize to unseen tasks given its natural crowdsourcing instructions. |
Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering (2024.naacl-long)
Copied to clipboard
Neha Srikanth, Rupak Sarkar, Heran Mane, Elizabeth Aparicio, Quynh Nguyen, Rachel Rudinger, Jordan Boyd-Graber
| Challenge: | a question-answering system must address pragmatic inferences to answer usefully, says a new study . human information needs are often inferred from the surface form, but answers must address the pragmatic needs of the question. |
| Approach: | They examine assumptions and implications made when mothers ask questions . they find that incorporating these inferences into QA pipelines produces more complete answers . |
| Outcome: | a study shows that incorporating inferences from questions helps to address harmful beliefs . human needs vary when asking questions, but a complete answer can address them . a QA pipeline can be more effective in addressing these needs, the study finds . |
Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition (2024.emnlp-main)
Copied to clipboard
| Challenge: | Prior research addresses generating attributions alongside responses in open domains, either per sentence or per paragraph. |
| Approach: | They propose a method to decompose generated answers for attribution using template-based in-context learning. |
| Outcome: | The proposed approach enhances the semantic understanding of abstractive and extractive answers. |