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检索条件"任意字段=3rd Workshop for Natural Language Processing Open Source Software, NLP-OSS 2023"

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3rd workshop for natural language processing open source software, nlp-oss 2023, Proceedings of the workshop

3rd Workshop for Natural Language Processing Open Source Sof...

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3rd workshop for natural language processing open source software, nlp-oss 2023

ISBN: (纸本)9798891760455

The proceedings contain 25 papers. The topics discussed include: Jina embeddings: a novel set of high-performance sentence embedding models;Deepparse : an extendable, and fine-tunable state-of-the-art library for parsing multinational street addresses;PyThainlp: Thai natural language processing in Python;Zelda Rose: a tool for hassle-free training of transformer models;Kani: a lightweight and highly hackable framework for building language model applications;beyond the Repo: a case study on open source integration with GECToR;two decades of the ACL anthology: development, impact, and open challenges;and nanoT5: fast & simple pre-training and fine-tuning of T5 models with limited resources.

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calamanCy: A Tagalog natural language processing Toolkit 3

calamanCy: A Tagalog Natural Language Processing Toolkit

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3rd workshop for natural language processing open source software, nlp-oss 2023

作者： Miranda, Lester James V. ExplosionAI GmbH Germany

ISBN: (纸本)9798891760455

We introduce calamanCy, an open-source toolkit for constructing natural language processing (nlp) pipelines for Tagalog. It is built on top of spaCy, enabling easy experimentation and integration with other frameworks. calamanCy addresses the development gap by providing a consistent API for building nlp applications and offering general-purpose multitask models with out-of-the-box support for dependency parsing, parts-of-speech (POS) tagging, and named entity recognition (NER). calamanCy aims to accelerate the progress of Tagalog nlp by consolidating disjointed resources in a unified framework. The calamanCy toolkit is available on GitHub: https://***/ljvmiranda921/calamanCy. © 2023 Association for Computational Linguistics.

关键词： natural language processing systems

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Beyond the Repo: A Case Study on open source Integration with GECToR 3

Beyond the Repo: A Case Study on Open Source Integration wit...

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3rd workshop for natural language processing open source software, nlp-oss 2023

作者： Kashyap, Sanjna Xie, Zhaoyang Steimel, Kenneth Madnani, Nitin Educational Testing Service PrincetonNJ United States

ISBN: (纸本)9798891760455

We present a case study describing our efforts to integrate the open source GECToR code and models into our production nlp pipeline that powers many of Educational Testing Service’s products and prototypes. The paper’s contributions includes a discussion of the issues we encountered during integration and our solutions, the overarching lessons we learned about integrating open source projects, and, last but not least, the open source contributions we made as part of the journey. © 2023 Association for Computational Linguistics.

关键词： open systems

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Using Captum to Explain Generative language Models 3

Using Captum to Explain Generative Language Models

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3rd workshop for natural language processing open source software, nlp-oss 2023

作者： Miglani, Vivek Yang, Aobo Markosyan, Aram H. Garcia-Olano, Diego Kokhlikyan, Narine Meta AI United States

ISBN: (纸本)9798891760455

Captum is a comprehensive library for model explainability in PyTorch, offering a range of methods from the interpretability literature to enhance users’ understanding of PyTorch models. In this paper, we introduce new features in Captum1 that are specifically designed to analyze the behavior of generative language models. We provide an overview of the available functionalities and example applications of their potential for understanding learned associations within generative language models. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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GPT4All: An Ecosystem of open source Compressed language Models 3

GPT4All: An Ecosystem of Open Source Compressed Language Mod...

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3rd workshop for natural language processing open source software, nlp-oss 2023

作者： Anand, Yuvanesh Nussbaum, Zach Treat, Adam Miller, Aaron Guo, Richard Schmidt, Ben Duderstadt, Brandon Mulyar, Andriy Nomic AI United States

ISBN: (纸本)9798891760455

Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure, are only accessible via rate-limited, geo-locked, and censored web interfaces, and lack publicly available code and technical reports. In this paper, we tell the story of GPT4All, a popular open source repository that aims to democratize access to LLMs. We outline the technical details of the original GPT4All model family, as well as the evolution of the GPT4All project from a single model into a fully fledged open source ecosystem. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem. © 2023 Association for Computational Linguistics.

关键词： Ecosystems

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PyTAIL: An open source Tool for Interactive and Incremental Learning of nlp Models with Human in the Loop for Online Data 3

PyTAIL: An Open Source Tool for Interactive and Incremental ...

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3rd workshop for natural language processing open source software, nlp-oss 2023

作者： Mishra, Shubhanshu Diesner, Jana *** India University of Illinois at Urbana-Champaign United States

ISBN: (纸本)9798891760455

Online data streams make training machine learning models hard because of distribution shift and new patterns emerging over time. For natural language processing (nlp) tasks that utilize a collection of features based on lexicons and rules, it is important to adapt these features to the changing data. To address this challenge we introduce PyTAIL, a python library, which allows a human in the loop approach to actively train nlp models. PyTAIL enhances generic active learning, which only suggests new instances to label by also suggesting new features like rules and lexicons to label. Furthermore, PyTAIL is flexible enough for users to accept, reject, or update rules and lexicons as the model is being trained. Finally, we simulate the performance of PyTAIL on existing social media benchmark datasets for text classification. We compare various active learning strategies on these benchmarks. The model closes the gap with as few as 10% of the training data. Finally, we also highlight the importance of tracking evaluation metric on remaining data (which is not yet merged with active learning) alongside the test dataset. This highlights the effectiveness of the model in accurately annotating the remaining dataset, which is especially suitable for batch processing of large unlabelled corpora. PyTAIL will be open sourced and available at https://***/socialmediaie/pytail. © 2023 Association for Computational Linguistics.

关键词： Batch data processing

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Antarlekhaka: A Comprehensive Tool for Multi-task natural language Annotation 3

Antarlekhaka: A Comprehensive Tool for Multi-task Natural La...

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3rd workshop for natural language processing open source software, nlp-oss 2023

作者： Terdalkar, Hrishikesh Bhattacharya, Arnab Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (纸本)9798891760455

One of the primary obstacles in the advancement of natural language processing (nlp) technologies for low-resource languages is the lack of annotated datasets for training and testing machine learning models. In this paper, we present Antarlekhaka, a tool for manual annotation of a comprehensive set of tasks relevant to nlp. The tool is Unicode-compatible, language-agnostic, Web-deployable and supports distributed annotation by multiple simultaneous annotators. The system sports user-friendly interfaces for 8 categories of annotation tasks. These, in turn, enable the annotation of a considerably larger set of nlp tasks. The task categories include two linguistic tasks not handled by any other tool, namely, sentence boundary detection and deciding canonical word order, which are important tasks for text that is in the form of poetry. We propose the idea of sequential annotation based on small text units, where an annotator performs several tasks related to a single text unit before proceeding to the next unit. The research applications of the proposed mode of multi-task annotation are also discussed. Antarlekhaka outperforms other annotation tools in objective evaluation. It has been also used for two real-life annotation tasks on two different languages, namely, Sanskrit and Bengali. The tool is available at https://***/Antarlekhaka/code. © 2023 Association for Computational Linguistics.

关键词： natural language processing systems

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PyThainlp: Thai natural language processing in Python 3

PyThaiNLP: Thai Natural Language Processing in Python

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3rd workshop for natural language processing open source software, nlp-oss 2023

作者： Phatthiyaphaibun, Wannaphong Chaovavanich, Korakot Polpanumas, Charin Suriyawongkul, Arthit Lowphansirikul, Lalita Chormai, Pattarawat Limkonchotiwat, Peerat Suntorntip, Thanathip Udomcharoenchaikit, Can VISTEC Thailand PyThaiNLP Thailand Trinity College Dublin Ireland Technische Universität Berlin Germany Max Planck School of Cognition Germany Wisesight Thailand

ISBN: (纸本)9798891760455

We present PyThainlp, a free and open-source natural language processing (nlp) library for Thai language implemented in Python. It provides a wide range of software, models, and datasets for Thai language. We first provide a brief historical context of tools for Thai language prior to the development of PyThainlp. We then outline the functionalities it provided as well as datasets and pre-trained language models. We later summarize its development milestones and discuss our experience during its development. We conclude by demonstrating how industrial and research communities utilize PyThainlp in their work. The library is freely available at https://***/pythainlp/pythainlp. © 2023 Association for Computational Linguistics.

关键词： Python

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DeepZensols: A Deep Learning natural language processing Framework for Experimentation and Reproducibility 3

DeepZensols: A Deep Learning Natural Language Processing Fra...

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3rd workshop for natural language processing open source software, nlp-oss 2023

作者： Landes, Paul Di Eugenio, Barbara Caragea, Cornelia Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891760455

Given the criticality and difficulty of reproducing machine learning experiments, there have been significant efforts in reducing the variance of these results. The ability to consistently reproduce results effectively strengthens the underlying hypothesis of the work and should be regarded as important as the novel aspect of the research itself. The contribution of this work is an open source framework that has the following characteristics: a) facilitates reproducing consistent results, b) allows hot-swapping features and embeddings without further processing and re-vectorizing the dataset, c) provides a means of easily creating, training and evaluating natural language processing deep learning models with little to no code changes, and d) is freely available to the community. © 2023 Association for Computational Linguistics.

关键词： Deep learning

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An open-source Web-based Application for Development of Resources and Technologies in Underresourced languages 3

An Open-source Web-based Application for Development of Reso...

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3rd workshop for natural language processing open source software, nlp-oss 2023

作者： Singh, Siddharth Ratan, Shyam Mathur, Neerav Kumar, Ritesh CTRANS Dr. Bhimrao Ambedkar University Agra India CALTS University of Hyderabad Hyderabad India UnReaL-TecE LLP Agra India

ISBN: (纸本)9798891760455

The paper discusses the Linguistic Field Data Management and Analysis System (LiFE), a new open-source, web-based software that systematises storage, management, annotation, analysis and sharing of linguistic data gathered from the field as well as that crawled from various sources on the web such as YouTube, Twitter, Facebook, Instagram, Blog, Newspaper, Wikipedia, etc. The app supports two broad workflows - (a) the field linguists’ workflow in which data is collected directly from the speakers in the field and analysed further to produce grammatical descriptions, lexicons, educational materials and possibly language technologies;(b) the computational linguists’ workflow in which data collected from the web using automated crawlers or digitised using manual or semi-automatic means, annotated for various tasks and then used for developing different kinds of language technologies. © 2023 Association for Computational Linguistics.

关键词： Application programs

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