Papers with selection process
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations (2020.coling-demos)
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| Challenge: | COLING 2020 is the 28th International Conference on Computational Linguistics held online due to the COVID-19 pandemic. |
| Approach: | a volume of papers from the online system demonstration session was published . the volume was published by the international committee on computational linguistics . |
| Outcome: | The volume contains papers from the system demonstration session held online due to the COVID-19 pandemic. |
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations (C18-2)
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| Challenge: | COLING 2018 is a conference on computational linguistics . the program committee accepted 35 papers out of 53 submissions based on quality of work and utility . |
| Approach: | a rigorous review process accepted 35 papers out of 53 submissions . the program committee consisted of 36 members and one chair from academia and industry . |
| Outcome: | the program committee accepted 35 papers out of 53 submissions . most accepted systems are user-interactive and feature rich graphical user interfaces based on the COLING 2018 program . |
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop) (2026.eacl-srw)
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| Challenge: | 185 submissions from students from around the world were accepted this year . jane mccarthy: rapid growth brought significant logistical challenges . she says the selection process was highly competitive and accepted 77 papers . |
| Approach: | 185 submissions from students from around the world were accepted this year . a rigorous double-blind review rated the papers originality, technical quality and clarity . |
| Outcome: | the EACL 2026 Student Research Workshop received 185 submissions from students . the event was held in the african region for the first time . |
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from related Example Banks (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive few-shot learning capabilities through in-context learning. |
| Approach: | They propose a novel Alternating Minimization approach for example selection that improves ICL performance on low-resource Indic languages. |
| Outcome: | The proposed approach outperforms existing frameworks for retrieving examples on low-resource Indic languages. |
Mitigating Hallucination by Integrating Knowledge Graphs into LLM Inference – a Systematic Literature Review (2025.acl-srw)
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| Challenge: | Large Language Models (LLMs) have made significant progress on different language tasks, but they tend to "hallucinate" plausible but factually incorrect answers. |
| Approach: | They propose to integrate knowledge graphs (KGs) into LLM inference to reduce hallucinations by searching online and applying a selection process. |
| Outcome: | The proposed integration improves performance on benchmark datasets and also to mitigate hallucinations. |
Boosting Textural NER with Synthetic Image and Instructive Alignment (2024.findings-acl)
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| Challenge: | Named entity recognition (NER) is a key task reliant on textual data. |
| Approach: | They propose a method to transform NER into a multimodal task by using images from the internet as auxiliaries. |
| Outcome: | The proposed method surpasses all text-only baselines and improves F1 score by 1.4% to 2.3% on prominent MNER datasets. |
MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity (2025.coling-main)
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| Challenge: | Existing RAG frameworks either indiscriminately perform retrieval or rely on rigid single-label classifiers to select retrieval methods. |
| Approach: | They propose a framework that dynamically selects the most suitable retrieval strategy based on query complexity. |
| Outcome: | The proposed framework achieves state-of-the-art results on multiple single-hop and multi-hop datasets while reducing retrieval costs. |
The Myth of Double-Blind Review Revisited: ACL vs. EMNLP (D19-1)
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| Challenge: | a double-blind review system enforces author anonymity during the review period . authors can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP . |
| Approach: | They examine how well deep learning techniques can infer authors of a paper . authors found authors can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP . |
| Outcome: | The proposed method can infer authors with 87% accuracy on ACL and 78% on EMNLP for the top 100 most prolific authors. |
Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm (2025.emnlp-main)
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| Challenge: | Existing studies focus on individual quality and do not assess the value of training data. |
| Approach: | They propose a choice-based sample selection framework that evaluates sample quality . they use LLMs to evaluate the value of each option during the selection process . |
| Outcome: | The proposed model outperforms the full dataset and recent studies on a larger medical dataset. |
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
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| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
| Approach: | They propose a retrieval-augmented large language model that can dynamically select the most suitable strategy based on query complexity. |
| Outcome: | The proposed approach improves the performance of QA systems on open-domain QA datasets. |
Cross-Lingual Event Detection via Optimized Adversarial Training (2022.naacl-main)
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| Challenge: | Recent work in this area has harnessed the language-invariant qualities of pre-trained Multi-lingual Language Models. |
| Approach: | They propose to use adversarial language adaptation to train a model to detect events in a target language. |
| Outcome: | The proposed model achieves state-of-the-art on 8 different language pairs, using 4 languages from unrelated families. |
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control (D19-1)
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| Challenge: | Selective rationalization is a common mechanism to ensure that predictive models reveal how they use any available features. |
| Approach: | They propose a co-operative method which uses introspection to explicitly predict and incorporate the outcome into the selection process. |
| Outcome: | The proposed model maintains high predictive accuracy and leads to comprehensive rationales. |
DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers (2025.emnlp-main)
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| Challenge: | Existing models focus on identifying relevant documents, but embedding similarity often limits accuracy. |
| Approach: | They propose a method to generate hard negative queries per page instead of negative pages per query . they propose to refine ranking of an initial set of retrieved documents using hard negative mining . |
| Outcome: | The proposed approach outperforms existing models and significantly improves retrieval performance. |
Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models (2025.coling-main)
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| Challenge: | Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination – generating content that does not align with realworld facts. |
| Approach: | They propose to extrapolate critical token probabilities beyond the last layer to improve decoding by manipulating the predicted distributions at inference time. |
| Outcome: | The proposed methods surpass state-of-the-art on multiple datasets by large margins. |
ParaICL: Towards Parallel In-Context Learning (2025.naacl-long)
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| Challenge: | Existing methods to improve ICL performance are limited by the length of the input context. |
| Approach: | They propose a method that utilizes all demonstration examples without exceeding the manageable context length. |
| Outcome: | The proposed method can be scaled up to integrate with existing methods. |
CrisPrune: Combining Contextual Relevance and Intrinsic Saliency for Efficient Visual Token Pruning in MLLMs (2026.findings-acl)
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| Challenge: | Existing methods for visual token pruning compromise the integrity of visual understanding in pursuit of efficiency. |
| Approach: | They propose a model-agnostic method that integrates visual saliency and text relevance to reconcile efficiency with understanding by integrating visual salions and text relevant. |
| Outcome: | The proposed method outperforms state-of-the-art methods on LLaVA-NeXT . it achieves 13 decrease in FLOPs while maintaining 97% of original performance . |
KidSpell: A Child-Oriented, Rule-Based, Phonetic Spellchecker (2020.lrec-1)
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Brody Downs, Oghenemaro Anuyah, Aprajita Shukla, Jerry Alan Fails, Sole Pera, Katherine Wright, Casey Kennington
| Challenge: | Existing spellcheckers are tuned to the needs of adults and are unsatisfactory for children due to their varying cognitive capabilities. |
| Approach: | They propose a model that maps misspelled words and spelling suggestions to their phonetic keys and a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word. |
| Outcome: | The proposed model outperforms existing spellcheckers in a number of offline experiments using existing and novel datasets. |