Papers with end-to-end system
Neural Mention Detection (2020.lrec-1)
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| Challenge: | Mention detection is an important preprocessing step for downstream applications such as NER and coreference resolution. |
| Approach: | They propose and compare three approaches to mention detection using ELMO embeddings and a biaffine classifier. |
| Outcome: | The proposed model outperforms state-of-the-art models on the GENIA corpora and improves on mention recall. |
AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling (2026.acl-demo)
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Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, Yue Zhang
| Challenge: | Existing automated systems for scientific illustrations are limited in editability, stylistic controllability, and efficiency. |
| Approach: | They propose an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. |
| Outcome: | The proposed system generates fully editable scientific illustrations from long-form scientific texts while enabling flexible style adaptation through user-provided reference images. |
CLARIESG: An End-to-End System for ESG Analysis over Complex Tables in Corporate Reports (2026.eacl-demo)
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Marta Santacroce, Michele Luca Contalbo, Sara Pederzoli, Riccardo Benassi, Venturelli Valeria, Matteo Paganelli, Francesco Guerra
| Challenge: | Sustainability reports contain rich Environmental, Social and Governance information, but their heterogeneous layouts and complex multi-table structures pose major challenges for LLMs, especially for unit normalization, cross-document reasoning, and precise numerical computation. |
| Approach: | They propose an end-to-end system that couples robust table extraction with a structured prompting framework for multi-table filtering, normalization, and program-of-thought reasoning. |
| Outcome: | The proposed system outperforms standard prompting on multi-table benchmarks and provides transparent, auditable reasoning. |
MetaPro Online: A Computational Metaphor Processing Online System (2023.acl-demo)
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| 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. |
Fluent Response Generation for Conversational Question Answering (2020.acl-main)
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| Challenge: | Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA subtask. |
| Approach: | They propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. |
| Outcome: | The proposed model outperforms baseline CoQA and QuAC models in generating conversational responses. |
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts (2022.emnlp-demos)
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Sonia Murthy, Kyle Lo, Daniel King, Chandra Bhagavatula, Bailey Kuehl, Sophie Johnson, Jonathan Borchardt, Daniel Weld, Tom Hope, Doug Downey
| Challenge: | Current systems that automatically define unfamiliar terms only surface a single "best" description for all users, which may not be accessible for all readers, given varying background knowledge. |
| Approach: | They propose an end-to-end system that generates sets of descriptions of scientific concepts . ACCoRD corpus includes 1,275 labeled contexts and 1,787 expert-authored concept descriptions . |
| Outcome: | The proposed system produces diverse descriptions of concepts in terms of reference concepts. |
MeetDot: Videoconferencing with Live Translation Captions (2021.emnlp-demo)
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Arkady Arkhangorodsky, Christopher Chu, Scot Fang, Yiqi Huang, Denglin Jiang, Ajay Nagesh, Boliang Zhang, Kevin Knight
| Challenge: | MeetDot is a videoconferencing system with live translation captions overlaid on screen . currently, the system supports speech and captions in 4 languages . |
| Approach: | They propose a videoconferencing system with live translation captions overlaid on screen . the system supports speech and captions in 4 languages and combines automatic speech recognition and machine translation in a cascade . |
| Outcome: | The proposed system supports speech and captions in 4 languages and has very tight latency requirements to have acceptable call quality. |
Autoregressive Entity Generation for End-to-End Task-Oriented Dialog (2022.coling-1)
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| Challenge: | Task-oriented dialog systems require external knowledge base to generate a response . current systems require scanning the KB at each turn, which is inefficient when the kb scales up . |
| Approach: | They propose to generate entity autoregressively before leveraging it to guide response generation. |
| Outcome: | Experiments on MultiWOZ 2.1 single and CAMREST show that the proposed system generates more high-quality and entity-consistent responses in an end-to-end manner. |
Personalized neural language models for real-world query auto completion (N18-3)
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| Challenge: | Existing popularity-based methods for query auto completion (QAC) are ineffective in predicting unseen queries. |
| Approach: | They propose to use real-world data to build an end-to-end system that can predict unseen queries by integrating user information. |
| Outcome: | The proposed methods improve on two separate datasets while increasing diversity while scalability. |
Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA (2026.acl-industry)
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Ummar Abbas, Mourad Ouzzani, Mohamed Y. Eltabakh, Omar Sinan, Gagan Bhatia, Hamdy Mubarak, Majd Hawasly, Mohammed Qusay Hashim, Kareem Mohamed Darwish, Firoj Alam
| Challenge: | Large language models (LLMs) can answer religious knowledge queries fluently, but they often hallucinate and misattribute sources. |
| Approach: | They propose a bilingual Arabic-English Islamic QA system that uses a multi-agent, tool-augmented architecture to route Islamic queries to specialized modules. |
| Outcome: | The proposed system is based on a multi-agent, tool-augmented architecture and has received over 1.9M accesses in less than a year. |
End-to-end Neural Information Status Classification (2021.findings-emnlp)
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| Challenge: | Existing studies on information status classification and bridging anaphora recognition assume that gold mention or syntactic tree information is given. |
| Approach: | They propose an end-to-end neural approach for information status classification using a mention extraction component and an information status assignment component. |
| Outcome: | The proposed system achieves state-of-the-art on fine-grained IS classification based on gold mentions and better than baselines on ISNotes and SciCorp. |
TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale (2026.acl-industry)
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| Challenge: | a 5minute downtime for an incident could result in a loss of 40 million dollars and erosion of user trust. |
| Approach: | They propose a multi-stage event unification engine that synergizes efficient indexing techniques with Large Language Models (LLMs) to make informed decisions on event merging. |
| Outcome: | The proposed system outperforms baseline methods in routing accuracy, clustering quality, and Signal-to-Noise Ratio. |
A Neural Question Answering Model Based on Semi-Structured Tables (C18-1)
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| Challenge: | Existing question answering systems rely on raw text and structured knowledge graphs. |
| Approach: | They build an end-to-end system to answer multiple choice questions with semi-structured tables as its knowledge. |
| Outcome: | The proposed system improves on the state-of-the-art question answering system with tabMCQ dataset. |
PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions (2020.findings-emnlp)
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| Challenge: | a novel machine translation-based approach to simplify prescription directions is proposed . the language used by physicians and health professionals includes medical jargon and implicit directives . |
| Approach: | They propose a machine translation-based approach to automatically and reliably simplify prescription directions into patient-friendly language. |
| Outcome: | The proposed system achieves a BLEU score of 60.27 over 530K prescriptions from a large mail-order pharmacy. |
VisCGEC: Benchmarking the Visual Chinese Grammatical Error Correction (2025.naacl-long)
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| Challenge: | Existing studies on Chinese grammatical error correction ignore multi-modality and faked errors, which pushes techniques far away from real-world scenarios. |
| Approach: | They propose to benchmark Chinese grammatical error correction for Chinese as a foreign language learner (CFL) using a dataset, they propose to use two CGEC frameworks to conduct experiments . |
| Outcome: | The proposed approach achieves an F 0.5 score of only 28.9%. |
CONSISTENT: Open-Ended Question Generation From News Articles (2022.findings-emnlp)
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| Challenge: | Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts. |
| Approach: | They propose an end-to-end system for generating openended questions that are answerable from and faithful to the input text. |
| Outcome: | The proposed model outperforms existing models and can be used in news media organizations. |
Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge (P19-1)
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| Challenge: | Existing methods to solve Winograd Schema Challenge use only knowledge embedded in text . this limits the performance of such models on the WSC problems. |
| Approach: | They propose to augment existing language models with a commonsense knowledge hunting module and an explicit reasoning module to extract the needed knowledge from text. |
| Outcome: | The proposed system improves on the language model based methods by 5.53% and 7.7% on the dataset. |
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering (2023.acl-long)
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| Challenge: | Recent advances in open-domain question answering have demonstrated impressive accuracy on general-purpose domains like Wikipedia. |
| Approach: | They propose a more realistic end-to-end domain shift evaluation setting covering five diverse domains to assess model adaption. |
| Outcome: | The proposed model improves by 24 points when adapted to unsupervised datasets. |
SpeechQE: Estimating the Quality of Direct Speech Translation (2024.emnlp-main)
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| Challenge: | Recent advances in automatic quality estimation for machine translation focus on written language, leaving the speech modality underexplored. |
| Approach: | They propose a new quality estimation system based on cascaded and end-to-end architectures. |
| Outcome: | The proposed system is better suited to estimating the quality of direct speech translation than existing systems designed for text translation. |