GenAI4ELab/papercli-papers huggingface dataset:
A Hyperparameter Optimization Toolkit for Neural Machine Translation Research
Authors: Xuan Zhang; Kevin Duh; Paul McNamee
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Hyperparameter optimization is an important but often overlooked process in the research of deep learning technologies. To obtain a good model, one must carefully tune hyperparameters that determine the architecture and training algorithm. Insufficient tuning may result in poor results, while inequitable tuning may lead to exaggerated differences between models. We present a hyperparameter optimization toolkit for neural machine translation (NMT) to help researchers focus their time on the creative rather than the mundane. The toolkit is implemented as a wrapper on top of the open-source Sockeye NMT software. Using the Asynchronous Successive Halving Algorithm (ASHA), we demonstrate that it is possible to discover near-optimal models under a computational budget with little effort. Code: https://github.com/kevinduh/sockeye-recipes3Video demo: https://cs.jhu.edu/kevinduh/j/demo.mp4
Japanese-to-English Simultaneous Dubbing Prototype
Authors: Xiaolin Wang; Masao Utiyama; Eiichiro Sumita
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Live video streaming has become an important form of communication such as virtual conferences. However, for cross-language communication in live video streaming, reading subtitles degrades the viewing experience. To address this problem, our simultaneous dubbing prototype translates and replaces the original speech of a live video stream in a simultaneous manner. Tests on a collection of 90 public videos show that our system achieves a low average latency of 11.90 seconds for smooth playback. Our method is general and can be extended to other language pairs.
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering
Authors: Zijun Yao; Yuanyong Chen; Xin Lv; Shulin Cao; Amy Xin; Jifan Yu; Hailong Jin; Jianjun Xu; Peng Zhang; Lei Hou; Juanzi Li
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries. VisKoP not only provides a neural program induction module, which converts natural language questions into knowledge oriented program language (KoPL), but also maps KoPL programs into graphical elements. KoPL programs can be edited with simple graphical operators, such as ”dragging” to add knowledge operators and ”slot filling” to designate operator arguments. Moreover, VisKoP provides auto-completion for its knowledge base schema and users can easily debug the KoPL program by checking its intermediate results. To facilitate the practical KBQA on a million-entity-level KB, we design a highly efficient KoPL execution engine for the back-end. Experiment results show that VisKoP is highly efficient and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. The VisKoP online demo, highly efficient KoPL engine, and screencast video are now publicly available.
PEEP-Talk: A Situational Dialogue-based Chatbot for English Education
Authors: Seungjun Lee; Yoonna Jang; Chanjun Park; Jungseob Lee; Jaehyung Seo; Hyeonseok Moon; Sugyeong Eo; Seounghoon Lee; Bernardo Yahya; Heuiseok Lim
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: English is acknowledged worldwide as a mode of communication. However, due to the absence of realistic practicing scenarios, students learning English as a foreign language (EFL) typically have limited chances to converse and share feedback with others. In this paper, we propose PEEP-Talk, a real-world situational dialogue-based chatbot designed for English education. It also naturally switches to a new topic or situation in response to out-of-topic utterances, which are common among English beginners. Furthermore, PEEP-Talk provides feedback score on conversation and grammar error correction. We performed automatic and user evaluations to validate performance and education efficiency of our system. The results show that PEEP-Talk generates appropriate responses in various real-life situations while providing accurate feedback to learners. Moreover, we demonstrate a positive impact on English-speaking, grammar, and English learning anxiety, implying that PEEP-Talk can lower the barrier to learning natural conversation in effective ways.
OpenTIPE: An Open-source Translation Framework for Interactive Post-Editing Research
Authors: Fabian Landwehr; Thomas Steinmann; Laura Mascarell
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Despite the latest improvements on machine translation, professional translators still must review and post-edit the automatic output to ensure high-quality translations. The research on automating this process lacks an interactive post-editing environment implemented for this purpose; therefore, current approaches do not consider the human interactions that occur in real post-editing scenarios. To address this issue, we present OpenTIPE, a flexible and extensible framework that aims at supporting research on interactive post-editing. Specifically, the interactive environment of OpenTIPE allows researchers to explore human-centered approaches for the post-editing task. We release the OpenTIPE source code and showcase 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
Authors: 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
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.
NeuroX Library for Neuron Analysis of Deep NLP Models
Authors: Fahim Dalvi; Hassan Sajjad; Nadir Durrani
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Neuron analysis provides insights into how knowledge is structured in representations and discovers the role of neurons in the network. In addition to developing an understanding of our models, neuron analysis enables various applications such as debiasing, domain adaptation and architectural search. We present NeuroX, a comprehensive open-source toolkit to conduct neuron analysis of natural language processing models. It implements various interpretation methods under a unified API, and provides a framework for data processing and evaluation, thus making it easier for researchers and practitioners to perform neuron analysis. The Python toolkit is available at https://www.github.com/fdalvi/NeuroX.Demo Video available at: https://youtu.be/mLhs2YMx4u8
SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation
Authors: Nianlong Gu; Richard H.R. Hahnloser
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Scientific writing involves retrieving, summarizing, and citing relevant papers, which can be time-consuming processes. Although in many workflows these processes are serially linked, there are opportunities for natural language processing (NLP) to provide end-to-end assistive tools. We propose SciLit, a pipeline that automatically recommends relevant papers, extracts highlights, and suggests a reference sentence as a citation of a paper, taking into consideration the user-provided context and keywords. SciLit efficiently recommends papers from large databases of hundreds of millions of papers using a two-stage pre-fetching and re-ranking literature search system that flexibly deals with addition and removal of a paper database. We provide a convenient user interface that displays the recommended papers as extractive summaries and that offers abstractively-generated citing sentences which are aligned with the provided context and which mention the chosen keyword(s). Our assistive tool for literature discovery and scientific writing is available at https://scilit.vercel.app
Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search
Authors: Chris Jenkins; Shantanu Agarwal; Joel Barry; Steven Fincke; Elizabeth Boschee
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: In this paper, we present ISI-Clear, a state-of-the-art, cross-lingual, zero-shot event extraction system and accompanying user interface for event visualization & search. Using only English training data, ISI-Clear makes global events available on-demand, processing user-supplied text in 100 languages ranging from Afrikaans to Yiddish. We provide multiple event-centric views of extracted events, including both a graphical representation and a document-level summary. We also integrate existing cross-lingual search algorithms with event extraction capabilities to provide cross-lingual event-centric search, allowing English-speaking users to search over events automatically extracted from a corpus of non-English documents, using either English natural language queries (e.g. “cholera outbreaks in Iran”) or structured queries (e.g. find all events of type Disease-Outbreak with agent “cholera” and location “Iran”).
YANMTT: Yet Another Neural Machine Translation Toolkit
Authors: Raj Dabre; Diptesh Kanojia; Chinmay Sawant; Eiichiro Sumita
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: In this paper, we present our open-source neural machine translation (NMT) toolkit called “Yet Another Neural Machine Translation Toolkit” abbreviated as YANMTT - https://github.com/prajdabre/yanmtt, which is built on top of the HuggingFace Transformers library. YANMTT focuses on transfer learning and enables easy pre-training and fine-tuning of sequence-to-sequence models at scale. It can be used for training parameter-heavy models with minimal parameter sharing and efficient, lightweight models via heavy parameter sharing. Additionally, it supports parameter-efficient fine-tuning (PEFT) through adapters and prompts. Our toolkit also comes with a user interface that can be used to demonstrate these models and visualize various parts of the model. Apart from these core features, our toolkit also provides other advanced functionalities such as but not limited to document/multi-source NMT, simultaneous NMT, mixtures-of-experts, model compression and continual learning.
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models
Authors: Dong-Ho Lee; Akshen Kadakia; Brihi Joshi; Aaron Chan; Ziyi Liu; Kiran Narahari; Takashi Shibuya; Ryosuke Mitani; Toshiyuki Sekiya; Jay Pujara; Xiang Ren
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: NLP models are susceptible to learning spurious biases (i.e., bugs) that work on some datasets but do not properly reflect the underlying task. Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, thenusing the feedback to update the model. While existing model debugging methods have shown promise, their prototype-level implementations provide limited practical utility. Thus, we propose XMD: the first open-source, end-to-end framework for explanation-based model debugging. Given task- or instance-level explanations,users can flexibly provide various forms of feedback via an intuitive, web-based UI. After receiving user feedback, XMD automatically updates the model in real time, by regularizing the model so that its explanationsalign with the user feedback. The new model can then be easily deployed into real-world applications via Hugging Face. Using XMD, we can improve the model’s OOD performance on text classification tasks by up to 18%.
OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models
Authors: Shengding Hu; Ning Ding; Weilin Zhao; Xingtai Lv; Zhen Zhang; Zhiyuan Liu; Maosong Sun
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: The scale of large pre-trained models (PTMs) poses significant challenges in adapting to downstream tasks due to the high optimization overhead and storage costs associated with full-parameter fine-tuning. To address this, many studies explore parameter-efficient tuning methods, also framed as “delta tuning” in Ding et al. (2022), which updates only a small subset of parameters, known as “delta modules”, while keeping the backbone model’s parameters fixed. However, the practicality and flexibility of delta tuning have been limited due to existing implementations that directly modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM. In this paper, we present OpenDelta, an open-source library that overcomes these limitations by providing a plug-and-play implementation of various delta tuning methods. Our novel techniques eliminate the need to modify the backbone PTMs’ code, making OpenDelta compatible with different, even novel PTMs. OpenDelta is designed to be simple, modular, and extensible, providing a comprehensive platform for researchers and practitioners to adapt large PTMs efficiently.
Hierarchy Builder: Organizing Textual Spans into a Hierarchy to Facilitate Navigation
Authors: Itay Yair; Hillel Taub-Tabib; Yoav Goldberg
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Information extraction systems often producehundreds to thousands of strings on a specifictopic. We present a method that facilitatesbetter consumption of these strings, in an ex-ploratory setting in which a user wants to bothget a broad overview of what’s available, and achance to dive deeper on some aspects. The sys-tem works by grouping similar items together,and arranging the remaining items into a hierar-chical navigable DAG structure. We apply themethod to medical information extraction.
CARE: Collaborative AI-Assisted Reading Environment
Authors: Dennis Zyska; Nils Dycke; Jan Buchmann; Ilia Kuznetsov; Iryna Gurevych
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Recent years have seen impressive progress in AI-assisted writing, yet the developments in AI-assisted reading are lacking. We propose inline commentary as a natural vehicle for AI-based reading assistance, and present CARE: the first open integrated platform for the study of inline commentary and reading. CARE facilitates data collection for inline commentaries in a commonplace collaborative reading environment, and provides a framework for enhancing reading with NLP-based assistance, such as text classification, generation or question answering. The extensible behavioral logging allows unique insights into the reading and commenting behavior, and flexible configuration makes the platform easy to deploy in new scenarios. To evaluate CARE in action, we apply the platform in a user study dedicated to scholarly peer review. CARE facilitates the data collection and study of inline commentary in NLP, extrinsic evaluation of NLP assistance, and application prototyping. We invite the community to explore and build upon the open source implementation of CARE.Github Repository: https://github.com/UKPLab/CAREPublic Live Demo: https://care.ukp.informatik.tu-darmstadt.de
The ROOTS Search Tool: Data Transparency for LLMs
Authors: Aleksandra Piktus; Christopher Akiki; Paulo Villegas; Hugo Laurençon; Gérard Dupont; Sasha Luccioni; Yacine Jernite; Anna Rogers
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: ROOTS is a 1.6TB multilingual text corpus developed for the training of BLOOM, currently the largest language model explicitly accompanied by commensurate data governance efforts. In continuation of these efforts, we present the ROOTS Search Tool: a search engine over the entire ROOTS corpus offering both fuzzy and exact search capabilities. ROOTS is the largest corpus to date that can be investigated this way. The ROOTS Search Tool is open-sourced and available on Hugging Face Spaces: https://huggingface.co/spaces/bigscience-data/roots-search. We describe our implementation and the possible use cases of our tool.
The OPUS-MT Dashboard – A Toolkit for a Systematic Evaluation of Open Machine Translation Models
Authors: Jörg Tiedemann; Ona de Gibert
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: The OPUS-MT dashboard is a web-based platform that provides a comprehensive overview of open translation models. We focus on a systematic collection of benchmark results with verifiable translation performance and large coverage in terms of languages and domains. We provide results for in-house OPUS-MT and Tatoeba models as well as external models from the Huggingface repository and user-contributed translations. The functionalities of the evaluation tool include summaries of benchmarks for over 2,300 models covering 4,560 language directions and 294 languages, as well as the inspection of predicted translations against their human reference. We focus on centralization, reproducibility and coverage of MT evaluation combined with scalability. The dashboard can be accessed live at https://opus.nlpl.eu/dashboard/.
The D-WISE Tool Suite: Multi-Modal Machine-Learning-Powered Tools Supporting and Enhancing Digital Discourse Analysis
Authors: Florian Schneider; Tim Fischer; Fynn Petersen-Frey; Isabel Eiser; Gertraud Koch; Chris Biemann
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: This work introduces the D-WISE Tool Suite (DWTS), a novel working environment for digital qualitative discourse analysis in the Digital Humanities (DH). The DWTS addresses limitations of current DH tools induced by the ever-increasing amount of heterogeneous, unstructured, and multi-modal data in which the discourses of contemporary societies are encoded. To provide meaningful insights from such data, our system leverages and combines state-of-the-art machine learning technologies from Natural Language Processing and Com-puter Vision. Further, the DWTS is conceived and developed by an interdisciplinary team ofcultural anthropologists and computer scientists to ensure the tool’s usability for modernDH research. Central features of the DWTS are: a) import of multi-modal data like text, image, audio, and video b) preprocessing pipelines for automatic annotations c) lexical and semantic search of documents d) manual span, bounding box, time-span, and frame annotations e) documentation of the research process.
OpenRT: An Open-source Framework for Reasoning Over Tabular Data
Authors: Yilun Zhao; Boyu Mi; Zhenting Qi; Linyong Nan; Minghao Guo; Arman Cohan; Dragomir Radev
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: There are a growing number of table pre-training methods proposed for reasoning over tabular data (e.g., question answering, fact checking, and faithful text generation). However, most existing methods are benchmarked solely on a limited number of datasets, varying in configuration, which leads to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents OpenRT, the first open-source framework for reasoning over tabular data, to reproduce existing table pre-training models for performance comparison and develop new models quickly. We implemented and compared six table pre-training models on four question answering, one fact checking, and one faithful text generation datasets. Moreover, to enable the community to easily construct new table reasoning datasets, we developed TaRAT, an annotation tool which supports multi-person collaborative annotations for various kinds of table reasoning tasks. The researchers are able to deploy the newly-constructed dataset to OpenRT and compare the performances of different baseline systems.
UINAUIL: A Unified Benchmark for Italian Natural Language Understanding
Authors: Valerio Basile; Livio Bioglio; Alessio Bosca; Cristina Bosco; Viviana Patti
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: This paper introduces the Unified Interactive Natural Understanding of the Italian Language (UINAUIL), a benchmark of six tasks for Italian Natural Language Understanding. We present a description of the tasks and software library that collects the data from the European Language Grid, harmonizes the data format, and exposes functionalities to facilitates data manipulation and the evaluation of custom models. We also present the results of tests conducted with available Italian and multilingual language models on UINAUIL, providing an updated picture of the current state of the art in Italian NLU.