Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models (2023.acl-demo)

Challenge:	a new open-source library for language-vision research and applications is available for free.
Approach:	They introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications.
Outcome:	The proposed library is open-source and highly extensible and configurable.

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Bum Chul Kwon, Nandana Mihindukulasooriya

PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development (2023.acl-demo)

Challenge:	Existing pre-trained transformer-based language models have shown exceptional performance on various benchmarks, but hidden biases can be found within these models.
Approach:	They propose a human-centered visual inspection tool to detect biases in different categories through log-likelihood scores generated by language models.
Outcome:	The proposed tool detects biases in different categories through log-likelihood scores generated by language models.

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Avi Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Juergen Bross, Radu Florian, Salim Roukos

Question Answering

Challenge:	Question Answering (QA) is a major area of research in Natural Language Processing (NLP)
Approach:	They propose a one-stop and open-source QA repository for question answering . it supports core QA functionalities like retrieval and reading comprehension . they say it will facilitate easy replication of state-of-the-art (SOTA) QA methods .
Outcome:	The proposed framework enables easy replication of state-of-the-art (SOTA) QA methods.

Lingxi: A Diversity-aware Chinese Modern Poetry Generation System (2023.acl-demo)

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Xinran Zhang, Maosong Sun, Jiafeng Liu, Xiaobing Li

Syntactic Text Processing

Challenge:	Chinese modern poetry generation is a challenging task because of the word segmentation problem and decoding methods . the decoding method may induce repetition and boredom and lower the diversity of generated poetry.
Approach:	They propose a Chinese word segmentation-based decoding system that incorporates Chinese word segments into tokenization.
Outcome:	The proposed system can achieve high vocabulary coverage rate with a reasonable vocabulary size.

Autodive: An Integrated Onsite Scientific Literature Annotation Tool (2023.acl-demo)

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Yi Du, Ludi Wang, Mengyi Huang, Dongze Song, Wenjuan Cui, Yuanchun Zhou

A Practical Toolkit for Multilingual Question and Answer Generation (2023.acl-demo)

Challenge:	Annotating scientific literature directly on PDF documents can greatly improve the labeling efficiency of scientists whose annotation costs are very high.
Approach:	They propose an integrated onsite scientific literature annotation tool for natural scientists and Natural Language Processing (NLP) researchers.
Outcome:	The proposed tool supports the whole lifecycle of corpus generation including i)project management, ii)resource management, and iv)ontology management, as well as manual annotation, onsite auto annotation, and vi)task statistic.

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Asahi Ushio, Fernando Alva-Manchego, Jose Camacho-Collados

Natural Language Interfaces

Challenge:	Generating questions and answers from text is a challenging task due to the expected structured output.
Approach:	They propose an online service for multilingual QAG along with a python package for model fine-tuning, generation, and evaluation.
Outcome:	The proposed model is available in eight languages and can be used online or locally via lmqg.

OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding (2023.acl-demo)

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Libo Qin, Qiguang Chen, Xiao Xu, Yunlong Feng, Wanxiang Che

SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes (2023.acl-demo)

Challenge:	Spoken Language Understanding (SLU) is a task-oriented dialogue system . open-source toolkit provides a unified, modularized, and extensible toolkit for SLU .
Approach:	They introduce an open-source toolkit to provide a unified toolkit for spoken language understanding.
Outcome:	The proposed toolkit unifies 10 models for both single-intent and multi-intention scenarios.

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Jivnesh Sandhan, Anshul Agarwal, Laxmidhar Behera, Tushar Sandhan, Pawan Goyal

Representation Learning

Challenge:	SanskritShala is a neural-based Sanskrit NLP toolkit that is available as a web-based application .
Approach:	They propose a neural Sanskrit NLP toolkit that facilitates linguistic analyses for word segmentation, morphological tagging, dependency parsing, and compound type identification.
Outcome:	The proposed toolkit reports state-of-the-art performance on benchmark datasets . it is built with easy-to-use interactive data annotation features .

LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models (2023.acl-demo)

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Victor Dibia

MetaPro Online: A Computational Metaphor Processing Online System (2023.acl-demo)

Challenge:	Existing research has sought to address these challenges by automating the visualization creation process, given a dataset.
Approach:	They propose a tool for generating grammar-agnostic visualizations and infographics using large language models and image generation models that address multiple tasks.
Outcome:	The proposed tool can generate grammar-agnostic visualizations and infographics from a dataset and python api.

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Rui Mao, Xiao Li, Kai He, Mengshi Ge, Erik Cambria

Challenge:	Metaphors do not take literal meanings in contexts, which may cause difficulties for language learners and machines to understand them.
Approach:	They propose a computational metaphor processing online system that queries metaphoricity labels, paraphrases and concept mappings for non-domain-specific text.
Outcome:	The proposed system can query metaphoricity labels, paraphrases, and concept mappings for non-domain-specific text without coding background.

DIAGRAPH: An Open-Source Graphic Interface for Dialog Flow Design (2023.acl-demo)

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Dirk Väth, Lindsey Vanderlyn, Ngoc Thang Vu

Dialogue Systems & Conversational Agents

Challenge:	Dialog systems have gained attention as a convenient way for users to access information in a more personalized manner.
Approach:	They present a graphical dialog flow editor built on ADVISER toolkit . it provides a clean and intuitive graphical interface for creating dialog systems .
Outcome:	The tool is based on the ADVISER toolkit and is evaluated with subject-experts . it is able to quickly prototype dialog systems and provide a test bed for students learning about dialog systems.

disco: a toolkit for Distributional Control of Generative Models (2023.acl-demo)

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Germán Kruszewski, Jos Rozen, Marc Dymetman

Challenge:	Pre-trained language models reproduce undesirable biases in training data and overlook patterns that are important but difficult to capture.
Approach:	They propose to use distributional control techniques to control the prevalence (i.e. expectations) of any features of interest in the model's outputs.
Outcome:	The proposed methods can control the prevalence (i.e. expectations) of features of interest in the model’s outputs.

A Hyperparameter Optimization Toolkit for Neural Machine Translation Research (2023.acl-demo)

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Xuan Zhang, Kevin Duh, Paul McNamee

Multilinguality

Challenge:	Insufficient tuning of hyperparameters may lead to poor results and exaggerated results . lack of open-source support tools means that the level of rigor in hyperparametric optimization may vary widely.
Approach:	They propose a hyperparameter optimization toolkit for neural machine translation that is implemented as a wrapper on top of the open-source Sockeye NMT software.
Outcome:	The proposed toolkit is implemented as a wrapper on top of the open-source Sockeye NMT software.

Japanese-to-English Simultaneous Dubbing Prototype (2023.acl-demo)

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Xiaolin Wang, Masao Utiyama, Eiichiro Sumita

VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)

Challenge:	Our system translates and replaces the original speech of a live video stream in a simultaneous manner.
Approach:	They propose a simultaneous dubbing prototype that translates and replaces the original speech of a live video stream in a simultaneous manner.
Outcome:	The proposed system achieves a low average latency of 11.90 seconds and meets a smoothness criterion.

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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li

PEEP-Talk: A Situational Dialogue-based Chatbot for English Education (2023.acl-demo)

Challenge:	Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) .
Approach:	They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries.
Outcome:	The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer.

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Seungjun Lee, Yoonna Jang, Chanjun Park, Jungseob Lee, Jaehyung Seo, Hyeonseok Moon, Sugyeong Eo, Seounghoon Lee, Bernardo Yahya, Heuiseok Lim

Natural Language Interfaces

Challenge:	Existing chatbots lack realistic practice scenarios for English learners . existing platforms employ hand-crafted and patternmatching rules, limiting communication ability and responding appropriately to out-of-situation utterances.
Approach:	They propose a real-world situational dialogue-based chatbot for English education . it generates appropriate responses in various real-life situations while providing accurate feedback .
Outcome:	The proposed chatbot generates appropriate responses in various real-life situations while providing accurate feedback to learners.

OpenTIPE: An Open-source Translation Framework for Interactive Post-Editing Research (2023.acl-demo)

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Fabian Landwehr, Thomas Steinmann, Laura Mascarell

Machine Translation

Challenge:	Recent advances in machine translation have not yet improved translation quality . human post-editors must review and post- edit the output to ensure high-quality translations . current approaches do not consider the human interactions that occur in real post- editing scenarios.
Approach:	They propose a flexible and extensible framework that supports research on interactive post-editing.
Outcome:	The proposed framework aims to support research on interactive post-editing . it showcases its main functionalities with a demonstration video and an online live demo .

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)

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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

NeuroX Library for Neuron Analysis of Deep NLP Models (2023.acl-demo)

Challenge:	Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures.
Approach:	They propose a toolkit that supports pre-training models of different modalities.
Outcome:	The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks.

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Fahim Dalvi, Hassan Sajjad, Nadir Durrani

SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation (2023.acl-demo)

Challenge:	NeuroX is an open-source toolkit to conduct neuron analysis of natural language processing models.
Approach:	They propose a Python toolkit to conduct neuron analysis of natural language processing models.
Outcome:	a new open-source toolkit enables neuron analysis of natural language processing models . the framework provides a framework for data processing and evaluation, making it easier for researchers and practitioners to perform neuron analyses.

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Nianlong Gu, Richard H.R. Hahnloser

Information Extraction & Text Mining

Challenge:	Scientific writing involves retrieving, summarizing, and citing relevant papers.
Approach:	They propose a pipeline that automatically recommends relevant papers, extracts highlights, and suggests a reference sentence as a citation of a paper.
Outcome:	The proposed pipeline recommends relevant papers from large databases of hundreds of millions of papers . it provides extractive summaries and abstractively-generated citation sentences . authors question whether it is possible to partly automate this process to reduce cognitive load .

Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search (2023.acl-demo)

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Chris Jenkins, Shantanu Agarwal, Joel Barry, Steven Fincke, Elizabeth Boschee

Information Retrieval

Challenge:	Using only English training data, ISI-Clear makes global events available on-demand in 100 languages . Using a fixed task, events may still shift from day to day .
Approach:	They propose a cross-lingual zero-shot event extraction system that makes global events available on-demand in 100 languages.
Outcome:	The proposed system can extract events from non-English documents in 100 languages.

YANMTT: Yet Another Neural Machine Translation Toolkit (2023.acl-demo)

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Raj Dabre, Diptesh Kanojia, Chinmay Sawant, Eiichiro Sumita

XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models (2023.acl-demo)

Challenge:	Neural machine translation (NMT) is an end-to-end approach that provides stateof-the-art results for a variety of language pairs.
Approach:	They propose to build an open-source neural machine translation toolkit on top of HuggingFace's Transformers library and use it for pre-training and fine-tuning sequence-to-sequence models.
Outcome:	The proposed toolkit is built on top of the HuggingFace Transformers library and provides advanced features such as document/multi-source NMT, simultaneous NMT and mixtures-of-experts.

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Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren

OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models (2023.acl-demo)

Challenge:	Existing models are susceptible to learning spurious biases that do not reflect the underlying task.
Approach:	They propose an open-source framework for explanation-based model debugging that allows users to provide various forms of feedback on model explanations.
Outcome:	The proposed framework improves model’s OOD performance on text classification tasks by up to 18%.

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Shengding Hu, Ning Ding, Weilin Zhao, Xingtai Lv, Zhen Zhang, Zhiyuan Liu, Maosong Sun

Hierarchy Builder: Organizing Textual Spans into a Hierarchy to Facilitate Navigation (2023.acl-demo)

Challenge:	Existing implementations that modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM have limited the practicality and flexibility of delta tuning.
Approach:	They propose an open-source library that provides a plug-and-play implementation of delta tuning methods for pre-trained models.
Outcome:	The proposed methods eliminate the need to modify the backbone PTMs’ code, making OpenDelta compatible with different, even novel PTM.

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Itay Yair, Hillel Taub-Tabib, Yoav Goldberg

Information Extraction & Text Mining

Challenge:	Information extraction systems produce hundreds to thousands of strings on a specific topic.
Approach:	They propose a method that allows users to consume a large collection of related textual strings in an exploratory mode.
Outcome:	The proposed method allows users to consume a large collection of related textual strings in an exploratory mode.

CARE: Collaborative AI-Assisted Reading Environment (2023.acl-demo)

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Dennis Zyska, Nils Dycke, Jan Buchmann, Ilia Kuznetsov, Iryna Gurevych

Challenge:	Recent years have seen impressive progress in AI-assisted writing, yet the developments in AI assisted reading are lacking.
Approach:	They propose an open integrated platform for the study of inline commentary and reading.
Outcome:	The proposed platform is used in a scholarly peer review study and invites the community to build upon it.

The ROOTS Search Tool: Data Transparency for LLMs (2023.acl-demo)

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Aleksandra Piktus, Christopher Akiki, Paulo Villegas, Hugo Laurençon, Gérard Dupont, Sasha Luccioni, Yacine Jernite, Anna Rogers

Information Retrieval

Challenge:	a 1.6TB multilingual text corpus is currently the largest language model . large language models are ubiquitous in modern NLP, used directly to generate text and as building blocks in downstream applications.
Approach:	They propose a search engine for the 1.6TB multilingual ROOTS corpus offering both fuzzy and exact search capabilities.
Outcome:	The ROOTS Search Tool is an open-source search engine for the 1.6TB multilingual ROOTs corpus.

The OPUS-MT Dashboard – A Toolkit for a Systematic Evaluation of Open Machine Translation Models (2023.acl-demo)

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Jörg Tiedemann, Ona de Gibert

Multilinguality

Challenge:	OPUS-MT dashboard provides a comprehensive overview of open translation models . the landscape of machine translation (MT) is increasingly blurry due to the growing volume of shared tasks and models published within the community.
Approach:	OPUS-MT dashboard provides a comprehensive overview of open translation models . dashboard includes summaries of benchmarks for over 2,300 models covering 4,560 languages . authors focus on centralization, reproducibility and coverage of MT evaluation combined with scalability .
Outcome:	OPUS-MT dashboard provides a comprehensive overview of open translation models . the evaluation tool includes summaries of benchmarks for over 2,300 models spanning 4,560 languages and 294 languages .

The D-WISE Tool Suite: Multi-Modal Machine-Learning-Powered Tools Supporting and Enhancing Digital Discourse Analysis (2023.acl-demo)

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Florian Schneider, Tim Fischer, Fynn Petersen-Frey, Isabel Eiser, Gertraud Koch, Chris Biemann

OpenRT: An Open-source Framework for Reasoning Over Tabular Data (2023.acl-demo)

Challenge:	The D-WISE Tool Suite addresses limitations of current DH tools due to the ever-increasing amount of heterogeneous, unstructured, and multi-modal data in which discourses of contemporary societies are encoded.
Approach:	They propose to use D-WISE Tool Suite to analyze heterogeneous, unstructured, and multi-modal data in the Digital Humanities (DH)
Outcome:	The proposed tool leverages state-of-the-art machine learning technologies from Natural Language Processing and Com-puter Vision to ensure its usability for modernDH research.

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Yilun Zhao, Boyu Mi, Zhenting Qi, Linyong Nan, Minghao Guo, Arman Cohan, Dragomir Radev

UINAUIL: A Unified Benchmark for Italian Natural Language Understanding (2023.acl-demo)

Challenge:	Existing table pre-training methods are benchmarked on a limited number of datasets with varying configurations, resulting in a lack of unified, standardized, fair, and comprehensive comparison between methods.
Approach:	They propose to use OpenRT to reproduce existing table pre-training models and develop new models quickly.
Outcome:	The proposed framework reproduces existing table pre-training models and compares them against four question answering, one fact checking, and one faithful text generation datasets.

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Valerio Basile, Livio Bioglio, Alessio Bosca, Cristina Bosco, Viviana Patti

Challenge:	a benchmark of six tasks for Italian Natural Language Understanding is presented . large language models (LLMs) have revolutionized the field of natural language processing . a few benchmarks exist for non-English languages, but only a handful are available for nonEnglish languages .
Approach:	They introduce a benchmark for Italian Natural Language Understanding that harmonizes the data format and exposes functionalities to facilitate data manipulation and evaluation of custom models.
Outcome:	The proposed benchmarks are based on the European Language Grid and available models in Italian and multilingual languages.

Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction (2023.acl-demo)

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Gabriele Picco, Marcos Martinez Galindo, Alberto Purpura, Leopold Fuchs, Vanessa Lopez, Thanh Lam Hoang

BiSync: A Bilingual Editor for Synchronized Monolingual Texts (2023.acl-demo)

Challenge:	ZSL is a machine learning field that uses textual descriptions of entities or relations to perform tasks that are not seen during training.
Approach:	They propose a framework that allows researchers to compare state-of-the-art ZSL methods with standard benchmark datasets.
Outcome:	The proposed framework compares state-of-the-art methods with benchmark datasets and provides APIs for production under the standard SpaCy NLP pipeline.

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Josep Crego, Jitao Xu, François Yvon

Riveter: Measuring Power and Social Dynamics Between Entities (2023.acl-demo)

Challenge:	CAT systems often interfere with writing process by requiring users to access external resources.
Approach:	They propose a bilingual writing assistant that allows users to freely compose text in two languages while maintaining the two monolingual texts synchronized.
Outcome:	The proposed bilingual writing assistant can produce high accuracy with limited computational resources.

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Maria Antoniak, Anjalie Field, Jimin Mun, Melanie Walsh, Lauren Klein, Maarten Sap

Fast Whitespace Correction with Encoder-Only Transformers (2023.acl-demo)

Challenge:	Riveter provides a complete pipeline for analyzing verb connotations associated with entities in text corpora.
Approach:	et al., 2005, provide a verb-centric analysis pipeline for verb connotations in text corpora . they prepopulate the pipeline with connotation frames of sentiment, power, and agency . lexical frameworks have been foundational tools in social science, digital humanities, and natural language processing .
Outcome:	Riveter provides a complete pipeline for analyzing verb connotations associated with entities in text corpora.

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Hannah Bast, Matthias Hertel, Sebastian Walter

ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit (2023.acl-demo)

Challenge:	a new tool for whitespace correction is available for text with spurious spaces . the tool is 900 times faster than the previous best tool for text correction .
Approach:	They propose to combine whitespace correction with a character-level encoder-decoder model and a byte-level byte encoder only model to improve quality.
Outcome:	The proposed tool is over 900 times faster than the previous best tool, with the same high quality.

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Brian Yan, Jiatong Shi, Yun Tang, Hirofumi Inaguma, Yifan Peng, Siddharth Dalmia, Peter Polák, Patrick Fernandes, Dan Berrebbi, Tomoki Hayashi, Xiaohui Zhang, Zhaoheng Ni, Moto Hira, Soumi Maiti, Juan Pino, Shinji Watanabe

CB2: Collaborative Natural Language Interaction Research Platform (2023.acl-demo)

Challenge:	ESPnet-ST-v2 is a revamp of the open-source spoken language translation toolkit . it supports offline speech-to-text translation (ST), simultaneous speech- to-text (SST), and offline speech to-speech (S2ST)
Approach:	They propose to revamp the open-source ESPnet-ST toolkit to support offline speech-to-text translation, simultaneous speech- to-text and offline speech to-speech translation.
Outcome:	The updated version of ESPnet-ST supports offline speech-to-text translation (ST), simultaneous speech- to-text (SST), and offline speech to-speech translation (S2ST).

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Jacob Sharf, Mustafa Omer Gul, Yoav Artzi

Challenge:	CB2 is a platform to study collaborative grounded natural language interactions in task-oriented scenarios . it includes a 3D game environment, backend server, tools and processes to enable scalable studies.
Approach:	They propose a multi-agent platform to study collaborative natural language interactions in a task-oriented scenario.
Outcome:	The proposed model is a demonstration of a collaborative natural language agent . it is based on the CEREALBAR scenario, but is scalable to emphasize accessibility .

Inseq: An Interpretability Toolkit for Sequence Generation Models (2023.acl-demo)

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Gabriele Sarti, Nils Feldhus, Ludwig Sickert, Oskar van der Wal

Pipeline for modeling causal beliefs from natural language (2023.acl-demo)

Challenge:	Recent studies focused on classification tasks while largely overlooking generation settings due to a lack of dedicated tools.
Approach:	They propose to use Inseq to democratize access to interpretability analyses of sequence generation models by enabling intuitive extraction of models’ internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures.
Outcome:	The proposed library can extract models’ internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures.

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John Priniski, Ishaan Verma, Fred Morstatter

Challenge:	Existing methods to analyze language data for psychological causality are difficult to advance as they do not isolate cognitive mechanisms.
Approach:	They propose a pipeline that leverages a Large Language Model to identify causal claims made in natural language documents and applies a clustering algorithm to group causal claims based on their semantic topics.
Outcome:	The proposed pipeline analyzes the Covid-19 vaccine in tweets and generates a causal claim network.

TabGenie: A Toolkit for Table-to-Text Generation (2023.acl-demo)

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Zdeněk Kasner, Ekaterina Garanina, Ondrej Platek, Ondrej Dusek

An Efficient Conversational Smart Compose System (2023.acl-demo)

Challenge:	TabGenie enables researchers to explore, preprocess, and analyze data-to-text generation datasets.
Approach:	They present TabGenie, a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets.
Outcome:	The toolkit provides an interactive mode for debugging table-to-text generation, side-by-side comparison of generated system outputs, and easy exports for manual analysis.

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Yun Zhu, Xiayu Chen, Lei Shu, Bowen Tan, Xinying Song, Lijuan Liu, Maria Wang, Jindong Chen, Ning Ruan

Dialogue Systems & Conversational Agents

Challenge:	a cloud-based smart compose system is designed to improve human-to-human conversation efficiency.
Approach:	They propose a cloud-based smart compose system to improve conversation efficiency . they propose heuristics to achieve the best trade-off between quality and latency .
Outcome:	The proposed system reduces latency without losing composing quality further.

Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals (2023.acl-demo)

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Robin Chan, Afra Amini, Mennatallah El-Assady

LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application (2023.acl-demo)

Challenge:	a spurious correlation exists when a feature correlates with the target label while there is no causal relationship between the feature and the label.
Approach:	They propose a dashboard that allows users to generate diverse and challenging examples by drawing inspiration from GPT-3 suggestions.
Outcome:	The proposed dashboard enables users to generate diverse and challenging examples by drawing inspiration from GPT-3 suggestions and make refinements based on the feedback.

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Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, Yong Zhang

Alfred: A System for Prompted Weak Supervision (2023.acl-demo)

Challenge:	Existing systems that translate optimization formulas manually are cumbersome and time-consuming.
Approach:	They propose a system that converts optimization formulas from TeX document to solver language.
Outcome:	The proposed system helps operations research practitioners convert optimization formulations into solver modeling languages.

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Peilin Yu, Stephen Bach

Low-Resource NLP

Challenge:	Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting.
Approach:	They propose to use Python to create training data by prompting for machine learning . they find that it improves query throughput by 2.9x versus a naive approach .
Outcome:	The proposed system improves query throughput by 2.9x versus a naive approach.

OpenICL: An Open-Source Framework for In-context Learning (2023.acl-demo)

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Zhenyu Wu, Yaoxiang Wang, Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Jingjing Xu, Yu Qiao

Self-Supervised Sentence Polishing by Adding Engaging Modifiers (2023.acl-demo)

Challenge:	In-context Learning (ICL) is a new paradigm for large language model evaluation.
Approach:	They propose an open-source toolkit for ICL and LLM evaluation.
Outcome:	The proposed framework is highly flexible and flexible and can be easily combined with other tools to suit users' needs.

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Zhexin Zhang, Jian Guan, Xin Cui, Yu Ran, Bo Liu, Minlie Huang

Effidit: An Assistant for Improving Writing Efficiency (2023.acl-demo)

Challenge:	a typical way to polish sentences is to add engaging modifiers, which enhance the meaning of the sentence.
Approach:	They propose a task that requires polishing sentences while maintaining fluency . they remove engaging modifiers from public resources and fine-tune LongLM to reconstruct original sentences from corrupted ones.
Outcome:	The proposed model generates more engaging sentences with suitable modifiers than strong baselines while keeping fluency.

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Shuming Shi, Enbo Zhao, Wei Bi, Deng Cai, Leyang Cui, Xinting Huang, Haiyun Jiang, Duyu Tang, Kaiqiang Song, Longyue Wang, Chenyan Huang, Guoping Huang, Yan Wang, Piji Li

Green & Sustainable NLP

Challenge:	Effidit is a digital writing assistant that provides three modules to help users write faster and more efficiently.
Approach:	They present Effidit, a digital writing assistant that provides three modules to help users write higher-quality text more efficiently.
Outcome:	Effidit expands the capabilities of a typical writing assistant by providing three modules . Effit can help users create their own text faster and more efficiently .

WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings (2023.acl-demo)

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Zijie J. Wang, Fred Hohman, Duen Horng Chau

Representation Learning

Challenge:	Modern machine learning models learn latent embedding representations that capture the domain semantics of training data.
Approach:	They propose an interactive visualization tool to help users explore large embeddings by using a multi-resolution summarization method and a familiar map-like interface.
Outcome:	The proposed visualization tool scales to millions of embedding points directly in users’ web browsers and computational notebooks without the need for dedicated backend servers.

A System for Answering Simple Questions in Multiple Languages (2023.acl-demo)

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Anton Razzhigaev, Mikhail Salnikov, Valentin Malykh, Pavel Braslavski, Alexander Panchenko

KWJA: A Unified Japanese Analyzer Based on Foundation Models (2023.acl-demo)

Challenge:	Existing knowledge graph question answering systems are limited to simple questions, but they can be used to answer complex questions.
Approach:	They propose a multilingual Knowledge Graph Question Answering technique that orders potential responses based on the distance between the question’s text embeddings and the answer’s graph embedds.
Outcome:	The proposed method consistently outperforms baseline systems, including seq2seq QA models and complex rule-based pipelines.

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Nobuhiro Ueda, Kazumasa Omura, Takashi Kodama, Hirokazu Kiyomaru, Yugo Murawaki, Daisuke Kawahara, Sadao Kurohashi

Challenge:	KWJA supports a wide range of tasks including typo correction, word segmentation, word normalization, named entity recognition, dependency parsing, PAS analysis, bridging reference resolution, coreference resolution, and discourse relation analysis.
Approach:	They propose to build a Japanese text analyzer based on foundation models that performs a wide range of tasks.
Outcome:	The proposed model performs better in a multi-task manner than other analyzers with specialized models.

Disease Network Constructor: a Pathway Extraction and Visualization (2023.acl-demo)

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Mohammad Golam Sohrab, Khoa Duong, Goran Topić, Masami Ikeda, Nozomi Nagano, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Itoh, Hiroya Takamura

Information Extraction & Text Mining

Challenge:	In the human body, various substances (entities) such as proteins and compounds interact and regulate each other, forming huge pathway networks.
Approach:	They present a system that extracts and visualizes a disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis.
Outcome:	The proposed system extracts and visualizes a disease network from biomedical articles on idiopathic pulmonary fibrosis (IPF) it includes two-dimensional (2D) and 3D visualizations of the constructed disease network.

Petals: Collaborative Inference and Fine-tuning of Large Models (2023.acl-demo)

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Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Maksim Riabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel

UKP-SQuARE v3: A Platform for Multi-Agent QA Research (2023.acl-demo)

Challenge:	Recent studies show that pretrained language models can solve practical tasks using more than 100 billion parameters.
Approach:	They propose a system for inference and fine-tuning of large models collaboratively by joining the resources of multiple parties.
Outcome:	The proposed system outperforms offloading for very large models running on consumer GPUs with 1 step per second, enough for many interactive LLM applications.

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Haritz Puerto, Tim Baumgärtner, Rachneet Sachdeva, Haishuo Fang, Hao Zhang, Sewin Tariverdian, Kexin Wang, Iryna Gurevych

Natural Language Interfaces

Challenge:	Current approaches to QA models are multi-dataset models, but combining expert agents can yield large performance gains over multi-agent models.
Approach:	They extend an online platform for QA research to support three families of multi-agent systems: agent selection, early-fusion of agents, and late-fusion.
Outcome:	The proposed model can be compared with multi-dataset models and achieve high inference speed and performance.

Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation (2023.acl-demo)

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Mete Sertkan, Sophia Althammer, Sebastian Hofstätter