Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations

7 papers
AMesure: A Web Platform to Assist the Clear Writing of Administrative Texts (2020.aacl-demo)

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Challenge: OECD, 2016) report that a significant proportion of citizens still have general reading difficulties.
Approach: They propose to use a readability formula and natural language processing tools to analyze texts and highlight linguistic phenomena considered difficult to read.
Outcome: The AMesure platform analyzes administrative texts and offers advice from plain language guides.
AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises (2020.aacl-demo)

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Challenge: AutoNLU is an on-demand cloud-based system that enables users to create and edit datasets and train and test different state-of-the-art NLU models.
Approach: They introduce an on-demand cloud-based system that provides an easy-to-use interface . they build powerful keyphrase extraction models that achieve state-of-the-art results .
Outcome: The proposed model achieves state-of-the-art on two public benchmarks and is easy to use and use.
ISA: An Intelligent Shopping Assistant (2020.aacl-demo)

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Challenge: In-store users only need to take a picture or scan the barcode of the product of interest, and then the user can talk to the assistant about the product.
Approach: They present a mobile-based intelligent shopping assistant that is designed to improve shopping experience in physical stores.
Outcome: The proposed system can improve shopping experience in physical stores by leveraging advanced techniques in computer vision, speech processing, and natural language processing.
metaCAT: A Metadata-based Task-oriented Chatbot Annotation Tool (2020.aacl-demo)

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Challenge: Creating high-quality annotated dialogue corpora necessitates a high level of human engagements.
Approach: They propose to develop an annotation tool specifically for developing task-oriented dialogue data that provides comprehensive metadata annotation coverage to the domain, intent, and span information.
Outcome: The tool provides comprehensive metadata annotation coverage to domain, intent, and span information.
NLP Tools for Predictive Maintenance Records in MaintNet (2020.aacl-demo)

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Challenge: Maintenance logbooks often contain free text fields with domain specific terms, abbreviations, and non-standard spelling . most standard NLP pipelines for pre-processing and annotation are trained on standard contemporary corpora.
Approach: They propose to create an open-source library and data repository for predictive maintenance language datasets and to evaluate the tools available at MaintNet.
Outcome: The proposed tools improve the performance of existing pipelines and improve the quality of the existing ones.
Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq (2020.aacl-demo)

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Challenge: End-to-end sequence-to sequence (S2S) modeling has witnessed rapid growth in speech-totext (ST) tasks.
Approach: They introduce fairseq S2T, a fairsq extension for speech-to-text modeling tasks such as end-to end speech recognition and speech-text translation.
Outcome: The proposed extension provides end-to-end workflows from data pre-processing, model training to offline (online) inference.
NLPStatTest: A Toolkit for Comparing NLP System Performance (2020.aacl-demo)

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Challenge: Statistical significance testing is used to compare NLP system performance, but p-values alone are insufficient because statistical significance differs from practical significance.
Approach: They propose a three-stage procedure for comparing NLP system performance and a toolkit that automates the process.
Outcome: The proposed procedure is based on a three-stage procedure and compares it with existing statistical testing toolkits.

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