Location-Aware Visual Question Generation with Lightweight Models Article Swipe
Nicholas Collin Suwono
,
Justin Chen
,
Tun Min Hung
,
Ting-Hao Huang
,
I-Bin Liao
,
Yung‐Hui Li
,
Lun‐Wei Ku
,
Shao-Hua Sun
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.emnlp-main.88
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.emnlp-main.88
This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location. Specifically, we represent such location-aware information with surrounding images and a GPS coordinate. To tackle this task, we present a dataset generation pipeline that leverages GPT-4 to produce diverse and sophisticated questions. Then, we aim to learn a lightweight model that can address the LocaVQG task and fit on an edge device, such as a mobile phone. To this end, we propose a method which can reliably generate engaging questions from location-aware information. Our proposed method outperforms baselines regarding human evaluation (e.g., engagement, grounding, coherence) and automatic evaluation metrics (e.g., BERTScore, ROUGE-2). Moreover, we conduct extensive ablation studies to justify our proposed techniques for both generating the dataset and solving the task.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2023.emnlp-main.88
- https://aclanthology.org/2023.emnlp-main.88.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389523798
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389523798Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18653/v1/2023.emnlp-main.88Digital Object Identifier
- Title
-
Location-Aware Visual Question Generation with Lightweight ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Nicholas Collin Suwono, Justin Chen, Tun Min Hung, Ting-Hao Huang, I-Bin Liao, Yung‐Hui Li, Lun‐Wei Ku, Shao-Hua SunList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2023.emnlp-main.88Publisher landing page
- PDF URL
-
https://aclanthology.org/2023.emnlp-main.88.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://aclanthology.org/2023.emnlp-main.88.pdfDirect OA link when available
- Concepts
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Computer science, Pipeline (software), Task (project management), Global Positioning System, Human–computer interaction, Artificial intelligence, Task analysis, Machine learning, Information retrieval, Data science, Telecommunications, Economics, Programming language, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.aims | 12 |
| abstract_inverted_index.both | 129 |
| abstract_inverted_index.data | 18 |
| abstract_inverted_index.edge | 75 |
| abstract_inverted_index.end, | 84 |
| abstract_inverted_index.from | 17, 95 |
| abstract_inverted_index.such | 28, 77 |
| abstract_inverted_index.task | 70 |
| abstract_inverted_index.that | 48, 65 |
| abstract_inverted_index.this | 40, 83 |
| abstract_inverted_index.with | 31 |
| abstract_inverted_index.work | 1 |
| abstract_inverted_index.GPT-4 | 50 |
| abstract_inverted_index.Then, | 57 |
| abstract_inverted_index.human | 104 |
| abstract_inverted_index.learn | 61 |
| abstract_inverted_index.model | 64 |
| abstract_inverted_index.novel | 4 |
| abstract_inverted_index.task, | 5, 41 |
| abstract_inverted_index.task. | 136 |
| abstract_inverted_index.which | 11, 89 |
| abstract_inverted_index.(e.g., | 106, 114 |
| abstract_inverted_index.images | 33 |
| abstract_inverted_index.method | 88, 100 |
| abstract_inverted_index.mobile | 80 |
| abstract_inverted_index.phone. | 81 |
| abstract_inverted_index.tackle | 39 |
| abstract_inverted_index.visual | 7 |
| abstract_inverted_index.LocaVQG | 69 |
| abstract_inverted_index.address | 67 |
| abstract_inverted_index.conduct | 119 |
| abstract_inverted_index.dataset | 45, 132 |
| abstract_inverted_index.device, | 76 |
| abstract_inverted_index.diverse | 53 |
| abstract_inverted_index.justify | 124 |
| abstract_inverted_index.metrics | 113 |
| abstract_inverted_index.present | 43 |
| abstract_inverted_index.produce | 52 |
| abstract_inverted_index.propose | 86 |
| abstract_inverted_index.solving | 134 |
| abstract_inverted_index.studies | 122 |
| abstract_inverted_index.ablation | 121 |
| abstract_inverted_index.engaging | 15, 93 |
| abstract_inverted_index.generate | 14, 92 |
| abstract_inverted_index.pipeline | 47 |
| abstract_inverted_index.proposed | 99, 126 |
| abstract_inverted_index.question | 8 |
| abstract_inverted_index.relevant | 19 |
| abstract_inverted_index.reliably | 91 |
| abstract_inverted_index.Moreover, | 117 |
| abstract_inverted_index.ROUGE-2). | 116 |
| abstract_inverted_index.automatic | 111 |
| abstract_inverted_index.baselines | 102 |
| abstract_inverted_index.extensive | 120 |
| abstract_inverted_index.leverages | 49 |
| abstract_inverted_index.location. | 24 |
| abstract_inverted_index.questions | 16, 94 |
| abstract_inverted_index.regarding | 103 |
| abstract_inverted_index.represent | 27 |
| abstract_inverted_index.(LocaVQG), | 10 |
| abstract_inverted_index.BERTScore, | 115 |
| abstract_inverted_index.coherence) | 109 |
| abstract_inverted_index.evaluation | 105, 112 |
| abstract_inverted_index.generating | 130 |
| abstract_inverted_index.generation | 9, 46 |
| abstract_inverted_index.grounding, | 108 |
| abstract_inverted_index.introduces | 2 |
| abstract_inverted_index.particular | 22 |
| abstract_inverted_index.questions. | 56 |
| abstract_inverted_index.techniques | 127 |
| abstract_inverted_index.coordinate. | 37 |
| abstract_inverted_index.engagement, | 107 |
| abstract_inverted_index.information | 30 |
| abstract_inverted_index.lightweight | 63 |
| abstract_inverted_index.outperforms | 101 |
| abstract_inverted_index.surrounding | 32 |
| abstract_inverted_index.geographical | 23 |
| abstract_inverted_index.information. | 97 |
| abstract_inverted_index.Specifically, | 25 |
| abstract_inverted_index.sophisticated | 55 |
| abstract_inverted_index.location-aware | 6, 29, 96 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.65430087 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |