Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations
VScript: Controllable Script Generation with Visual Presentation (2022.aacl-demo)
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Ziwei Ji, Yan Xu, I-Tsun Cheng, Samuel Cahyawijaya, Rita Frieske, Etsuko Ishii, Min Zeng, Andrea Madotto, Pascale Fung
| Challenge: | Using a script generation system, scriptwriters can customize their scripts using video retrieval. |
| Approach: | They propose a controllable pipeline that generates complete scripts, including dialogues and scene descriptions, and presents visually using video retrieval. |
| Outcome: | The proposed system outperforms baselines on both automatic and human evaluations, especially in genre control. |
TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation (2022.aacl-demo)
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Lorenz Stangier, Ji-Ung Lee, Yuxi Wang, Marvin Müller, Nicholas Frick, Joachim Metternich, Iryna Gurevych
| Challenge: | TexPrax is a messaging system to collect and annotate task-oriented dialog data . informal communication channels such as instant messengers are increasingly being used at work . |
| Approach: | They propose a messaging system that collects and annotates task-oriented dialog data from employees via chatbots. |
| Outcome: | The proposed system collects and annotates tasks-oriented dialog data from german factory workers and provides lightweight annotations. |
PicTalky: Augmentative and Alternative Communication for Language Developmental Disabilities (2022.aacl-demo)
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| Challenge: | Existing software packages are expensive and difficult to use, and only provide simple functions. |
| Approach: | They propose an AI-based AAC system called PicTalky that can improve communication skills for children with language disabilities. |
| Outcome: | The proposed system improves communication skills and language comprehension abilities for children with language disabilities. |
UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA (2022.aacl-demo)
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Rachneet Sachdeva, Haritz Puerto, Tim Baumgärtner, Sewin Tariverdian, Hao Zhang, Kexin Wang, Hossain Shaikh Saadi, Leonardo F. R. Ribeiro, Iryna Gurevych
| Challenge: | Question Answering (QA) systems rely on deep neural networks, which are difficult to interpret by humans. |
| Approach: | They propose an interpretable model that provides an explanation infrastructure for comparing models based on saliency maps and graph-based explanations. |
| Outcome: | The proposed methods can be used to compare models based on saliency maps and graph-based explanations. |
TaxFree: a Visualization Tool for Candidate-free Taxonomy Enrichment (2022.aacl-demo)
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| Challenge: | In this paper, we present an open source system for taxonomy visualisation and automatic taxonomies enrichment without pre-defined candidates. |
| Approach: | They propose an open source system for taxonomy visualisation and automatic taxonomie enrichment without pre-defined candidates on the example of WordNet-3.0. |
| Outcome: | The proposed system can be used for visualisation and inspection of taxonomies without pre-defined candidates on WordNet-3.0. |
F-coref: Fast, Accurate and Easy to Use Coreference Resolution (2022.aacl-demo)
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| Challenge: | Existing models for coreference resolution are difficult to implement, consume a lot of GPU memory and take long to process each document. |
| Approach: | They propose a python package for fast, accurate, and easy-to-use English coreference resolution. |
| Outcome: | The proposed model can process 2.8K OntoNotes documents in 25 seconds on a V100 GPU, compared to 6 minutes for the LingMess model and 12 minutes of the popular AllenNLP coreference model. |
PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature (2022.aacl-demo)
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| Challenge: | Existing literature search engines cannot deliver recipe steps of the literature . manual processing and assimilating useful information is expensive and time-consuming for researchers. |
| Approach: | They propose a machine learning-based procedural information extraction and knowledge management system that extracts procedural recipe steps, figures, and tables from materials science articles. |
| Outcome: | The proposed system extracts procedural information recipe steps, figures, and tables from materials science articles and provides information retrieval capability and statistics visualization functionality. |
BiomedCurator: Data Curation for Biomedical Literature (2022.aacl-demo)
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Mohammad Golam Sohrab, Khoa N.A. Duong, Ikeda Masami, Goran Topić, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Nogami Itoh, Hiroya Takamura
| Challenge: | BiomedCurator uses state-of-the-art natural language processing techniques to extract structured data from scientific articles. |
| Approach: | They propose a web application that extracts structured data from PubMed and ClinicalTrials.gov . the application uses a combination of natural language processing techniques and a pattern-based extraction approach . |
| Outcome: | The proposed system extracts the structured data from PubMed and ClinicalTrials.gov datasets. |
Text Characterization Toolkit (TCT) (2022.aacl-demo)
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Daniel Simig, Tianlu Wang, Verna Dankers, Peter Henderson, Khuyagbaatar Batsuren, Dieuwke Hupkes, Mona Diab
| Challenge: | Text Characterization Toolkit (TCT) is a tool that researchers can use to study characteristics of large datasets. |
| Approach: | They propose a text characterization toolkit that researchers can use to study characteristics of large datasets. |
| Outcome: | The proposed tool can be used to study characteristics of large datasets and to understand the influence of attributes on models’ behaviour. |
Meeting Decision Tracker: Making Meeting Minutes with De-Contextualized Utterances (2022.aacl-demo)
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| Challenge: | Existing systems to itemize meetings' decisions are lacking in their raw form due to utterance collapse. |
| Approach: | They propose a prototype system to construct decision items that deal with utterance collapse in natural conversation. |
| Outcome: | The proposed system improves the user experience by dealing with utterance collapse in natural conversation. |