| Challenge: | a dataset of over 50,000 natural language commands captures various phenomena, including functional references, relational reasoning, and visual reasoning. |
| Approach: | They propose a task that requires the user to choose the correct element on a web page . they use a dataset of over 50,000 natural language commands to map these to web pages . |
| Outcome: | The proposed task can be viewed as a reference game based on a dataset of over 50,000 natural language commands . |
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Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction (2021.naacl-main)
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| Challenge: | Scholarly work in this area uses toy worlds and synthetic linguistic data, but grounded language learning offers several practical and scientific advantages. |
| Approach: | They propose to model teacher-learner dynamics through natural interactions occurring between users and search engines. |
| Outcome: | The proposed model is better than non-grounded models on compositionality and zero-shot inference tasks. |
High Performance Natural Language Processing (2020.emnlp-tutorials)
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| Challenge: | a tutorial on scaling natural language processing will recapitulate the state-of-the-art in the field . |
| Approach: | This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective. |
| Outcome: | This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective. |
FLIN: A Flexible Natural Language Interface for Web Navigation (2021.naacl-main)
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| Challenge: | Existing semantic parsing and slot-filling techniques cannot adapt to many different websites without being constantly re-trained. |
| Approach: | They propose a natural language interface for web navigation that maps user commands to concept-level actions rather than low-level UI actions. |
| Outcome: | The proposed interface can adapt to new websites in a given domain. |
LLM-driven Instruction Following: Progresses and Concerns (2023.emnlp-tutorial)
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| Challenge: | a tutorial on task instruction is aimed at researchers and practitioners interested in NLP generalization . labeled examples are unlikely to be available in large numbers or do not exist . |
| Approach: | This tutorial will examine the progress of natural language processing (NLP) using labeled examples. authors propose that task instructions act as a novel resource for supervision. |
| Outcome: | This tutorial aims to answer questions about instruction-driven NLP . it focuses on the use of task instructions in a low-shot scenario . |
Multimodal Grounding for Language Processing (C18-1)
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| Challenge: | Recent developments in multimodal processing facilitate conceptual grounding of language. |
| Approach: | They analyze multimodal processing to examine the benefits and challenges of multimodal grounding . they focus on multimodal linguistic grounding of verbs which play a crucial role in compositional power of language. |
| Outcome: | The proposed methods improve the cognitive models of human information processing and address the challenges that arise. |
Learning Language through Grounding (2025.naacl-tutorial)
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| Challenge: | This tutorial provides a historical overview of grounding and discusses its use in computational linguistics and in computational language processing. |
| Approach: | They introduce the concept of grounding and discuss future directions and open challenges . they will delve into recent progress in learning lexical semantics, syntax, and complex meanings through various forms of ground. |
| Outcome: | This course will provide an overview of the field of grounding and discuss future directions and challenges related to large language models and scaling. |
Efficient Methods for Natural Language Processing: A Survey (2023.tacl-1)
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Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz
| Challenge: | Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data, but using only scale to improve performance means resource consumption also grows. |
| Approach: | They propose to use data, time, storage, or energy to improve model performance. |
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Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (2025.emnlp-demos)
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| Challenge: | Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing are now available online. |
| Approach: | EMNLP 2025 conference on empirical methods in natural language processing held in Suzhou, china, on November 4-9, 2025. 77 papers accepted for inclusion in proceedings, resulting in 38% acceptance rate. |
| Outcome: | Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing are published . the conference accepted 77 papers, with a 38% acceptance rate . |
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (D18-2)
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| Challenge: | 77 submissions were received for the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) 4 of the 73 valid submissions received were either invalid or withdrawn by the authors. |
| Approach: | The volume contains papers from the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) 4 of the 77 submissions were either invalid or withdrawn by the authors. |
| Outcome: | The system demonstrations session included papers from the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) 4 of the 73 valid submissions were either invalid or withdrawn by the authors. |
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (2021.emnlp-demo)
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| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |