Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
DoTAT: A Domain-oriented Text Annotation Tool (2022.acl-demo)
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| Challenge: | DoTAT is a domain-oriented text annotation tool that can reduce the time for event annotation by 19.7% . the tool supports multi-person collaborative process with automatically merging and review . |
| Approach: | They propose a domain-oriented text annotation tool called DoTAT . it provides multi-person collaborative process with automatic merging and review . |
| Outcome: | The proposed tool can reduce the time for event annotation by 19.7% compared with existing tools. |
UKP-SQUARE: An Online Platform for Question Answering Research (2022.acl-demo)
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Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva, Gregor Geigle, Max Eichler, Clifton Poth, Hannah Sterz, Haritz Puerto, Leonardo F. R. Ribeiro, Jonas Pfeiffer, Nils Reimers, Gözde Şahin, Iryna Gurevych
| Challenge: | Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats and require different model architectures and setups. |
| Approach: | They propose an extensible online QA platform that allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. |
| Outcome: | The proposed tool allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. |
ViLMedic: a framework for research at the intersection of vision and language in medical AI (2022.acl-demo)
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Jean-benoit Delbrouck, Khaled Saab, Maya Varma, Sabri Eyuboglu, Pierre Chambon, Jared Dunnmon, Juan Zambrano, Akshay Chaudhari, Curtis Langlotz
| Challenge: | Multimodal medical AI is a growing field of interest, especially for tasks that involve multimodal data. |
| Approach: | They propose a vision-and-language medical library to improve multimodal medical predictions and enable new applications. |
| Outcome: | The vision-and-language medical library aims to improve reproducibility and speed up progress across medical AI . it contains a dozen implementations replicating state-of-the-art results on medical datasets . the library is extensible by researchers but also simple for practitioners . |
TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models (2022.acl-demo)
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| Challenge: | Large pre-trained language models have been used for many NLP tasks but computational resources are limited. |
| Approach: | They propose an open-source model pruning toolkit for pre-trained language models . they propose a self-supervised pruning method that can be applied without labeled data. |
| Outcome: | The proposed pruning method reduces model size without retraining the model and speeds up inference speed on the common CPU and GPU devices. |
AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark (2022.acl-demo)
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Niklas Friedrich, Kiril Gashteovski, Mingying Yu, Bhushan Kotnis, Carolin Lawrence, Mathias Niepert, Goran Glavaš
| Challenge: | Open Information Extraction (OIE) is the task of extracting facts from sentences in the form of relations and their corresponding arguments in schema-free manner. |
| Approach: | They propose an interactive annotation platform that facilitates annotating complete facts from input sentences. |
| Outcome: | The proposed platform facilitates such challenging annotation tasks and supports creation of fact-oriented OIE evaluation benchmarks. |
AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters (2022.acl-demo)
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Tilman Beck, Bela Bohlender, Christina Viehmann, Vincent Hane, Yanik Adamson, Jaber Khuri, Jonas Brossmann, Jonas Pfeiffer, Iryna Gurevych
| Challenge: | AdapterHub Playground is an open-access tool for researchers to use pretrained language models without writing a single line of code. |
| Approach: | They propose a tool which allows researchers to leverage pretrained models without writing a single line of code for a variety of NLP tasks. |
| Outcome: | The proposed model can be used for prediction, training and analysis of textual data without writing a single line of code. |
QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input (2022.acl-demo)
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| Challenge: | Existing systems based on attributes or keywords render lyrics generation very limited . previous studies focused on generating lyrics based only on attributes and keywords . |
| Approach: | They propose to use Chinese passage-level text as input for lyrics generation . they initialize parameters with custom pretrained Chinese GPT-2 model and adopt a two-step process to fine-tune the model for better alignment between passage- level text and lyrics. |
| Outcome: | The proposed system is conditioned on passage-level text rather than attributes or keywords, rendering limited control over the content of the lyrics. |
Automatic Gloss Dictionary for Sign Language Learners (2022.acl-demo)
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| Challenge: | 430 million people worldwide have developed hearing loss and 700 million more are learning a sign language as a second language . sign language learners have limited means of seeking assistance and are restricted to class offerings or relying on a webcam to look up the sign. |
| Approach: | They propose an online tool supporting 2, 000 signs to assist language learners in determining the meaning of given signs. |
| Outcome: | The proposed system can lower the barrier in sign language learning by addressing the common problem of sign finding and make it accessible to the wider community. |
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts (2022.acl-demo)
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Stephen Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-david, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Fries, Maged Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir Radev, Mike Tian-jian Jiang, Alexander Rush
| Challenge: | PromptSource is a system for creating, sharing, and using natural language prompts . prompts are used to train and query language models in zero-shot learning settings . |
| Approach: | PromptSource is a system for creating, sharing, and using natural language prompts . et al.: using prompts to train and query language models is emerging area in NLP . they propose a templating language for defining data-linked prompts, a user interface that iterates on prompt development . |
| Outcome: | PromptSource is a system for creating, sharing, and using natural language prompts . it has a templating language for defining data-linked prompts and a community-driven set of guidelines . |
OpenPrompt: An Open-source Framework for Prompt-learning (2022.acl-demo)
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| Challenge: | Prompt-learning is a new paradigm in natural language processing, adapting pre-trained language models to cloze-style prediction, autoregressive modeling, or sequence to sequence generation. |
| Approach: | They propose a framework for prompt-learning that integrates pre-trained language models with a unified framework. |
| Outcome: | The proposed framework is easy to use and flexible enough to integrate with other frameworks. |
Guided K-best Selection for Semantic Parsing Annotation (2022.acl-demo)
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Anton Belyy, Chieh-yang Huang, Jacob Andreas, Emmanouil Antonios Platanios, Sam Thomson, Richard Shin, Subhro Roy, Aleksandr Nisnevich, Charles Chen, Benjamin Van Durme
| Challenge: | a prototype model trained on a small amount of data is not available, leading to limited prediction performance. |
| Approach: | They propose a human-in-the-loop process that generates a set of valid candidates and allows users to quickly traverse the set and filter incorrect parses. |
| Outcome: | The proposed process can be used to efficiently traverse the candidate set and select the correct parse, with minimal modification when necessary. |
Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa (2022.acl-demo)
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| Challenge: | Developing better methods for a task is a common feature of the computational linguistics literature. |
| Approach: | They propose to use bootstrap to compute significance levels with the BOOtSTrap SAmpling procedure to evaluate models that predict hard labels and soft labels as well. |
| Outcome: | The proposed method can be used to evaluate models that predict hard labels and soft labels on benchmark data sets. |
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System (2022.acl-demo)
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Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji
| Challenge: | a new system extracts supporting and refuting claims from COVID-19 related news . the system is publicly available at GitHub and DockerHub, with complete documentation. |
| Approach: | They propose a COVID-19 Claim Radar system that extracts supporting and refuting claims . the system leverages Wikidata as the hub to consolidate coreferential knowledge elements . |
| Outcome: | The system extracts supporting and refuting claims from COVID-19 pandemic information . it leverages Wikidata as the hub to merge coreferential knowledge elements . |
TS-ANNO: An Annotation Tool to Build, Annotate and Evaluate Text Simplification Corpora (2022.acl-demo)
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| Challenge: | Currently, high-quality corpora of this type are rare and often of comparably small size. |
| Approach: | They propose an open-source web application for automatic text simplification. |
| Outcome: | TS-ANNO can be used for i) sentence–wise alignment, ii) rating alignment pairs, w.r.t. simplification transformations, and iv) manual simplification of complex documents. |
Language Diversity: Visible to Humans, Exploitable by Machines (2022.acl-demo)
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Gábor Bella, Erdenebileg Byambadorj, Yamini Chandrashekar, Khuyagbaatar Batsuren, Danish Cheema, Fausto Giunchiglia
| Challenge: | Universal Knowledge Core (UKC) is a large multilingual lexical database with a focus on language diversity and covering over two thousand languages. |
| Approach: | Universal Knowledge Core is a large multilingual lexical database with a focus on language diversity and covering over two thousand languages. |
| Outcome: | the database lets users explore millions of individual words and their meanings, but also phenomena of cross-lingual convergence and divergence, such as shared interlingual meanings and lexicon similarities. |
CogKGE: A Knowledge Graph Embedding Toolkit and Benchmark for Representing Multi-source and Heterogeneous Knowledge (2022.acl-demo)
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Zhuoran Jin, Tianyi Men, Hongbang Yuan, Zhitao He, Dianbo Sui, Chenhao Wang, Zhipeng Xue, Yubo Chen, Jun Zhao
| Challenge: | Existing methods focus on entity-centric knowledge, but CogKGE supports heterogeneous knowledge. |
| Approach: | They propose a knowledge graph embedding toolkit to represent multi-source and heterogeneous knowledge. |
| Outcome: | The proposed toolkit provides a unified programming framework for KGE tasks and a series of knowledge representations for downstream tasks. |
Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks (2022.acl-demo)
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Tristan Thrush, Kushal Tirumala, Anmol Gupta, Max Bartolo, Pedro Rodriguez, Tariq Kane, William Gaviria Rojas, Peter Mattson, Adina Williams, Douwe Kiela
| Challenge: | Open source system for setting up custom NLP tasks aims to lower technical knowledge and effort required for hosting and evaluating state-of-the-art models. |
| Approach: | They propose to integrate Dynatask with Dynabench to simplify benchmarking . they use a dataset to collect and clean data and train and evaluate models . |
| Outcome: | Dynatask is an open source system for setting up custom NLP tasks . it is integrated with Dynabench, a research platform for rethinking benchmarking in AI . |
DataLab: A Platform for Data Analysis and Intervention (2022.acl-demo)
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Yang Xiao, Jinlan Fu, Weizhe Yuan, Vijay Viswanathan, Zhoumianze Liu, Yixin Liu, Graham Neubig, Pengfei Liu
| Challenge: | Existing tools and research focus on how to interpret and manipulate data, despite its crucial role in machine learning, . existing tools and researchers focus on systems on top of existing data, rather than how to use it. |
| Approach: | They propose a unified data-oriented platform that allows users to interactively analyze the characteristics of data and provides a standard interface for many data processing operations. |
| Outcome: | The proposed platform allows users to analyze the characteristics of data and provides a standardized interface so that many data processing operations can be provided within a single interface. |
Cue-bot: A Conversational Agent for Assistive Technology (2022.acl-demo)
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Shachi H Kumar, Hsuan Su, Ramesh Manuvinakurike, Maximilian C. Pinaroc, Sai Prasad, Saurav Sahay, Lama Nachman
| Challenge: | Large-scale pre-training has achieved significant performance gains across many tasks within NLP, including intent prediction and dialogue state tracking. |
| Approach: | They propose to use eye-tracking, mouse controls and an intelligent agent Cue-bot to represent the user in a conversation. |
| Outcome: | The proposed system can be used by people with different levels of disabilities to interact with the world, supported by eye-tracking, mouse controls and an intelligent agent Cue-bot. |
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis (2022.acl-demo)
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| Challenge: | M-SENA is an open-source platform for multimodal sentiment analysis. |
| Approach: | They propose to use a platform for multimodal sentiment analysis to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations. |
| Outcome: | The proposed framework provides reliable benchmarks and baseline results of different modality features and MSA benchmarks. |
HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese (2022.acl-demo)
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| Challenge: | Existing studies have explored the use of entity linking (EL) in downstream tasks. |
| Approach: | They propose a modularized entity linking toolkit for easy task adaptation. |
| Outcome: | The proposed toolkit achieves significantly better accuracy and less time and spaceconsumption than existing methods. |
BMInf: An Efficient Toolkit for Big Model Inference and Tuning (2022.acl-demo)
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Xu Han, Guoyang Zeng, Weilin Zhao, Zhiyuan Liu, Zhengyan Zhang, Jie Zhou, Jun Zhang, Jia Chao, Maosong Sun
| Challenge: | Recent years, pre-trained language models (PLMs) have achieved promising results on various NLP tasks. |
| Approach: | They propose an open-source toolkit for big model inference and tuning which can support big model tuning at extremely low computation cost. |
| Outcome: | The proposed toolkit can support big model inference and tuning at extremely low computation cost. |
MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities (2022.acl-demo)
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Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, Jing Shao
| Challenge: | Recent Knowledge Graphs (KGs) store billions of world facts in a directed graph, but expression ability of such entity-centric KGs is limited. |
| Approach: | They propose a large-scale multi-modal event knowledge graph named MMEKG that unifies different modalities of knowledge via events. |
| Outcome: | The proposed system unifies different modalities of knowledge via events, which complement and disambiguate each other. |
SocioFillmore: A Tool for Discovering Perspectives (2022.acl-demo)
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| Challenge: | SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event. |
| Approach: | They propose a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event. |
| Outcome: | The proposed tool can be used by non-NLP researchers and is based on a large collection of human judgements. |
TimeLMs: Diachronic Language Models from Twitter (2022.acl-demo)
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| Challenge: | Neural language models (LMs) are a key enabler in NLP, but lack of diachronic specialization affects both the ability to generalize to future data and the reliability of experimental results. |
| Approach: | They propose to use Twitter data to develop time-specific language models that are specialized on the time variable. |
| Outcome: | The proposed models cope with trends and peaks in activity involving specific named entities or concept drift. |
Adaptor: Objective-Centric Adaptation Framework for Language Models (2022.acl-demo)
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| Challenge: | Adaptor library aims to simplify complex training processes requiring customizations. |
| Approach: | They introduce Adaptor library which transposes traditional model-centric approach to objective-centric training pipeline with Objective as central abstraction. |
| Outcome: | The proposed framework simplifies training processes and improves reproducibility. |
QuickGraph: A Rapid Annotation Tool for Knowledge Graph Extraction from Technical Text (2022.acl-demo)
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| Challenge: | Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators. |
| Approach: | They propose a supervised MT-IE annotation tool built with indirect weak supervision and clustering to maximise annotator productivity. |
| Outcome: | The proposed tool is compared with existing tools in the field of MT-IE and aims to increase annotator productivity. |