Francisco Rodrigues, Rinaldo Lima, William Domingues, Robson Fidalgo, Adrian Chifu, Bernard Espinasse, Sébastien Fournier
| Challenge: | Many third-party NLP tools perform distinct NLP subtasks, but integration is difficult . authors present a framework that enables easy integration of third-parties into a pipeline . |
| Approach: | They propose a framework that enables easy integration of third-party NLP tools . it provides an API for complete pipeline customization including definition of input/output formats . |
| Outcome: | The proposed framework reduces runtime processing time compared to executing the same pipeline in a sequential manner. |
Similar Papers
CogCompNLP: Your Swiss Army Knife for NLP (L18-1)
Copied to clipboard
Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
| Challenge: | a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks. |
| Approach: | They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community . |
| Outcome: | The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges. |
Dive into Deep Learning for Natural Language Processing (D19-2)
Copied to clipboard
| Challenge: | GluonNLP is a powerful new toolkit that automates the most laborious aspects of deep learning for NLP. |
| Approach: | This hands-on tutorial demonstrates how to scale unsupervised pre-training techniques with Apache MXNet and GluonNLP. |
| Outcome: | This hands-on tutorial examines the challenges of scaling these models and algorithms effectively with Apache MXNet and GluonNLP. |
NLP Lean Programming Framework: Developing NLP Applications More Effectively (N18-5)
Copied to clipboard
| Challenge: | NLPf is a framework for creating custom natural language processing models and pipelines by utilizing common software development build systems. |
| Approach: | They propose a framework for creating custom NLP models and pipelines by utilizing common software development build systems. |
| Outcome: | This framework allows developers to train and integrate domain-specific NLP pipelines into their applications seamlessly. |
NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools (2023.eacl-demo)
Copied to clipboard
| Challenge: | NLP Workbench is a web-based text mining platform that allows non-expert users to obtain semantic understanding of large-scale corpora using state-of-the-art text mining models. |
| Approach: | They propose to use a microservice architecture to replace existing models or integrate a new one. |
| Outcome: | The proposed model is extensible and can be easily replaced or integrated with existing models. |
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) (D19-61)
Copied to clipboard
| Challenge: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . |
| Approach: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . call for papers for this second workshop met with a strong response . |
| Outcome: | the EMNLP-IJCNLP 2019 workshop on deep learning approaches for low-resource natural language processing takes place in Hong Kong, China. |
A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)
Copied to clipboard
Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu
| Challenge: | Empirical natural language processing (NLP) systems involve interoperation among multiple components . a wealth of NLP toolkits exist ( 4), such as spaCy, DKPro, CoreNLP. |
| Approach: | They propose a unified open-source framework that supports fast development of NLP workflows . framework includes processors for NLP tasks, visualization, and annotation . |
| Outcome: | The framework offers processors for NLP tasks, visualization, and annotation, and is extensible . it is delivered through two modularized yet integratable open-source projects, Forte and Stave . |
XNLP: An Interactive Demonstration System for Universal Structured NLP (2024.acl-demos)
Copied to clipboard
| Challenge: | Structured Natural Language Processing (XNLP) is an important subset of NLP that entails understanding the underlying semantic or syntactic structure of texts. |
| Approach: | They propose a XNLP demonstration system that leverages LLM to achieve universal XnLP with one model for all with high generalizability. |
| Outcome: | The proposed system advances in multiple aspects, including universal XNLP modeling, high performance, interpretability, scalability, and interactivity. |
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing (2021.eacl-demos)
Copied to clipboard
| Challenge: | Trankit is a lightweight, pre-trained toolkit for multilingual natural language processing. |
| Approach: | They propose a transformer-based toolkit for multilingual natural language processing that trains pipelines over 100 languages and 90 pretrained pipelines for 56 languages. |
| Outcome: | The proposed tool outperforms existing pipelines over sentence segmentation, part-of-speech tagging, morphological feature tabbing, and dependency parsing while maintaining competitive performance over tokenization, multi-word token expansion, and lemmatization over 90 Universal Dependencies treebanks. |
DocNLI: A Large-scale Dataset for Document-level Natural Language Inference (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on sentence-level inference, which limits its application in downstream NLP problems. |
| Approach: | They propose to construct a large-scale dataset for document-level NLI that can be used to study NLP problems. |
| Outcome: | The proposed model performs well on popular sentence-level benchmarks and generalizes well to out-of-domain NLP tasks that rely on inference at document granularity. |
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing (2022.emnlp-demos)
Copied to clipboard
Chengyu Wang, Minghui Qiu, Taolin Zhang, Tingting Liu, Lei Li, Jianing Wang, Ming Wang, Jun Huang, Wei Lin
| Challenge: | Pre-Trained Models (PTMs) have reshaped the development of natural language processing (NLP) but it is not easy to obtain high-performing PTMs without a large amount of labeled training data and deploy them online with fast inference speed. |
| Approach: | They propose to make it easy to build NLP applications with knowledge-enhanced pre-training and knowledge distillation. |
| Outcome: | EasyNLP supports a comprehensive suite of NLP algorithms and features knowledge-enhanced pre-training, knowledge distillation and few-shot learning functionalities. |