Papers with easy-to-
AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises (2020.aacl-demo)
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| Challenge: | AutoNLU is an on-demand cloud-based system that enables users to create and edit datasets and train and test different state-of-the-art NLU models. |
| Approach: | They introduce an on-demand cloud-based system that provides an easy-to-use interface . they build powerful keyphrase extraction models that achieve state-of-the-art results . |
| Outcome: | The proposed model achieves state-of-the-art on two public benchmarks and is easy to use and use. |
Data2Text Studio: Automated Text Generation from Structured Data (D18-2)
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| Challenge: | Data2Text Studio is a platform for automated text generation from structured data. |
| Approach: | They conduct experiments on RotoWire datasets for template extraction and text generation . they find that the Semi-HMMs model improves interactivity and interpretability . |
| Outcome: | The proposed model improves on template extraction and text generation tasks on RotoWire datasets. |
N-LTP: An Open-source Neural Language Technology Platform for Chinese (2021.emnlp-demo)
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| Challenge: | Existing tools that teach an independent model for each task are not supported in Chinese. |
| Approach: | They propose an open-source neural language platform supporting six Chinese NLP tasks . source code, documentation, and pre-trained models are available at https://github.com/hit-SCIR/ltp . |
| Outcome: | The proposed platform supports six Chinese NLP tasks. |
A Practical Toolkit for Multilingual Question and Answer Generation (2023.acl-demo)
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| 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. |
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models (2024.acl-demos)
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Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) have improved performance across tasks and domains . instruction tuning is a crucial technique to enhance the capabilities of LLMs - but there is no standard open-source instruction processing framework available for the community . |
| Approach: | They propose an open-source instruction tuning framework for Large Language Models that modularizes instruction generation, selection, prompting and their combination and interaction. |
| Outcome: | The proposed framework is open-source and available on Github. |
YATO: Yet Another deep learning based Text analysis Open toolkit (2023.emnlp-demo)
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| Challenge: | YATO is an open-source toolkit for text analysis with deep learning . it supports free combinations of three types of widely used features . |
| Approach: | They introduce YATO, an open-source toolkit for text analysis with deep learning. |
| Outcome: | YATO is an open-source toolkit for text analysis with deep learning . the toolkit supports free combinations of three types of widely used features . |
VnCoreNLP: A Vietnamese Natural Language Processing Toolkit (N18-5)
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| Challenge: | Using word segmenters and POS taggers, Vietnamese NLP pipelines are no longer considered SOTA models for Vietnamese. |
| Approach: | They propose a Java NLP annotation pipeline for Vietnamese that provides rich linguistic annotations. |
| Outcome: | The proposed toolkit provides rich linguistic annotations to facilitate research work on Vietnamese NLP. |
TrainX – Named Entity Linking with Active Sampling and Bi-Encoders (2020.coling-demos)
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Tom Oberhauser, Tim Bischoff, Karl Brendel, Maluna Menke, Tobias Klatt, Amy Siu, Felix Alexander Gers, Alexander Löser
| Challenge: | Existing easyto-use annotation tools do not support entity linking, which leads to additional training costs for medical professionals. |
| Approach: | They propose a system for Named Entity Linking for medical experts . they use Flair and BERT to support annotating training data with active sampling . |
| Outcome: | The proposed system is capable of linking against large knowledge bases and supporting zero-shot cases where the linker has never seen the entity before. |
CLEVA: Chinese Language Models EVAluation Platform (2023.emnlp-demo)
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Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael Lyu, Liwei Wang
| Challenge: | Large language models (LLMs) have revolutionized natural language processing. |
| Approach: | They propose a Chinese-based platform that assesses Chinese LLMs using a standardized workflow and a unique sampling strategy. |
| Outcome: | CLEVA evaluates Chinese LLMs on a standardized workflow and a competitive leaderboard with minimal coding. |
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models (2024.findings-naacl)
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| Challenge: | Existing generative language models neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones. |
| Approach: | They propose a function to mitigate the imbalance between frequent and infrequent tokens . authors propose 'MiLe Loss' function to assess learning difficulty of tokens during training . |
| Outcome: | Experiments show that models with proposed model can improve on downstream benchmarks. |
OLEA: Tool and Infrastructure for Offensive Language Error Analysis in English (2023.eacl-demo)
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| Challenge: | State-of-the-art models for identifying offensive language fail to generalize over nuanced or implicit cases of offensive and hateful language. |
| Approach: | They propose an open-source Python library for error analysis in the context of offensive language detection. |
| Outcome: | OLEA provides tools for error analysis in the context of detecting offensive language in English. |
Learn With Martian: A Tool For Creating Assignments That Can Write And Re-Write Themselves (2023.eacl-demo)
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| Challenge: | Using existing course materials, Learn generates questions, selects the best questions, shows them to students, adapts difficulty to student knowledge, and improves as it collects more data on student performance. |
| Approach: | They propose a unified, easy-to-use tool to apply question generation and selection in classrooms. |
| Outcome: | The proposed tool can generate questions, select the best questions, show them to students, adapt difficulty to student knowledge, and improve as it collects more data on student performance. |
InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles (2021.emnlp-demo)
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| Challenge: | InVeRo-XL is an off-the-shelf system capable of annotating text with predicate sense and semantic role labels from 7 predicated-argument structure inventories in more than 40 languages. |
| Approach: | They propose to use RESTful API and Web interface to integrate sentence-level semantics into cross-lingual downstream tasks. |
| Outcome: | The proposed system can annotate text with predicate sense and semantic role labels from 7 predicated-argument structure inventories in more than 40 languages. |
Getting To Know You: User Attribute Extraction from Dialogues (2020.lrec-1)
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| Challenge: | a new method to extract user attributes from dialogues is needed to improve user understanding. |
| Approach: | They propose to leverage dialogues with conversational agents to automatically extract user attributes from dialogues. |
| Outcome: | The proposed model surpasses retrieval and generation baselines on human evaluation. |
EZCAT: an Easy Conversation Annotation Tool (2022.lrec-1)
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| Challenge: | EZCAT is an annotation tool for textual conversations, but it is not customizable. |
| Approach: | They propose an easy-to-use interface to annotate conversations in a configurable schema . they use it to annnotate private chats and chats, and they use the schema to test it . |
| Outcome: | The proposed interface allows users to control data and annotate conversations in two levels . it eliminates the need for a server and accounts management, and allows users access to data . |
word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs (2020.lrec-1)
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| Challenge: | Our dataset provides top-k word translations in 3,564 (directed) language pairs across 62 languages in OpenSubtitles2018. |
| Approach: | They propose a dataset and an open-source Python package for cross-lingual word translations extracted from sentence-level parallel corpora. |
| Outcome: | The proposed bilingual lexicons have high coverage and achieve competitive translation quality for several language pairs. |
Best-k Search Algorithm for Neural Text Generation (2023.acl-long)
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| Challenge: | Modern natural language generation paradigms require a decoding strategy to obtain quality sequences out of the model. |
| Approach: | They propose a deterministic search algorithm balancing quality and diversity . they investigate the vanilla best-first search algorithm and propose k-k search algorithm. |
| Outcome: | The proposed algorithm is parameter-free, lightweight, efficient, and easy-to-use. |
Language Models Don’t Know What You Want: Evaluating Personalization in Deep Research Needs Real Users (2026.acl-long)
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Nishant Balepur, Malachi Hamada, Varsha Kishore, Sergey Feldman, Amanpreet Singh, Pao Siangliulue, Joseph Chee Chang, Eunsol Choi, Jordan Lee Boyd-Graber, Aakanksha Naik
| Challenge: | Earlier research used real users to push personalization, but easy-to-use judges have been criticized for not adopting online studies. |
| Approach: | They propose a personalized action-following tool that infers a user's research interests and proposes personalized actions for a query. |
| Outcome: | The proposed tool beats baselines in citation metrics and personalized action-following with an online version of MySQA. |
QuASE: Question-Answer Driven Sentence Encoding (2020.acl-main)
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| Challenge: | Question-answering (QA) data often encodes essential information in many facets . a growing interest of QA has led to many large-scale QA datasets available to the community . |
| Approach: | They propose a question-answer driven sentence encoding framework to learn representations from QA data. |
| Outcome: | The proposed framework learns representations from QA data, using BERT or other state-of-the-art contextual language models. |
Identifying Noise in Human-Created Datasets using Training Dynamics from Generative Models (2025.findings-emnlp)
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| Challenge: | Existing noise detection techniques for autoencoder models do not generalize to ArLMs due to differences in learning dynamics. |
| Approach: | They propose a method that leverages training dynamics to rank datapoints from easy-to-learn to hard-tolear . TDRanker achieves at least 2x faster denoising than previous techniques . |
| Outcome: | The proposed method demonstrates robustness across multiple model architectures and noise levels. |
Model Calibration for Emotion Detection (2025.findings-emnlp)
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| Challenge: | a MixUp method is used to calibrate emotion detection models based on knowledge distillation and the MixUp data augmentation technique. |
| Approach: | They propose a method that uses knowledge distillation and the MixUp data augmentation technique to enhance the trustworthiness of emotion detection models. |
| Outcome: | The proposed method improves the accuracy of the teacher models and the student models. |
T5Score: A Methodology for Automatically Assessing the Quality of LLM Generated Multi-Document Topic Sets (2025.findings-acl)
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| Challenge: | Existing evaluation methods for Multi-Document Topic Extraction are not designed for LLMs and result in low inter-annotator agreement scores. |
| Approach: | They propose an evaluation methodology that decomposes the quality of a topic set into quantifiable aspects, measurable through easy-to-perform annotation tasks. |
| Outcome: | The proposed evaluation methodology decomposes the quality of a topic set into quantifiable aspects, measurable through easy-to-perform annotation tasks. |