Papers with Learning
Fast Word Predictor for On-Device Application (2020.coling-demos)
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| Challenge: | a proposed word prediction model is developed for a chat application serving more than 100 million users. |
| Approach: | They propose a fast word predictor that reduces memory size and inference time on mobile devices. |
| Outcome: | The proposed model reduces memory size and inference time on a mobile device compared with a standard neural network . it achieves robust performance by learning on large text corpora and is available on microsoft's chat app . |
Neural Transition-based String Transduction for Limited-Resource Setting in Morphology (C18-1)
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| Challenge: | Morphological string transduction involves mapping one word form into another, possibly given a feature specification for the mapping. |
| Approach: | They propose a neural transition-based model that uses a simple set of edit actions for morphological transduction tasks such as reinflection and reinflation. |
| Outcome: | The proposed model outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. |
Learning to Represent Image and Text with Denotation Graph (2020.emnlp-main)
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| Challenge: | Recent advances in learning representations of visual and language information have been a problem with many applications. |
| Approach: | They propose to extract visual expressions from images aligned with linguistic expressions that describe the images to learn representations from implicit expressions. |
| Outcome: | The proposed representations lead to stronger empirical results on downstream tasks of cross-modal image retrieval, referring expression, and compositional attribute-object recognition. |
Planning and Editing What You Retrieve for Enhanced Tool Learning (2024.findings-naacl)
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| Challenge: | Existing methods for integrating external tools with Large Language Models fall short on effectively shortlisting relevant tools. |
| Approach: | They propose a plan-and-retrieve and edit-and ground paradigms for LLMs that decompose complex queries into actionable tasks. |
| Outcome: | The proposed paradigms significantly improve recall and NDCG in tool retrieval tasks, surpassing current state-of-the-art models. |
Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation (2020.findings-emnlp)
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| Challenge: | Existing models treat STOP as other actions, which leads to undesirable behaviors that the agent fails to stop at the destination. |
| Approach: | They propose a policy module that differentiates STOP from other actions . they propose 'learning to stop' module that can be used to train an agent to follow natural language instructions in real-world environments. |
| Outcome: | The proposed model outperforms the baseline on a challenging urban VLN dataset Touchdown by 6.89%. |
Intent Recognition in Doctor-Patient Interviews (2020.lrec-1)
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| Challenge: | Currently, up to 20 percent of patients are misdiagnosed in medical training programs. |
| Approach: | They propose to annotate doctor-patient interviews with intent inventory and information retrieval methods that are robust with respect to small amounts of training data. |
| Outcome: | The proposed models provide baseline performance scores on the data set for further research. |
Adversarial Contrastive Estimation (P18-1)
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| Challenge: | Noise contrastive estimation (NCE) is a general strategy used in word embeddings and translations for knowledge graphs. |
| Approach: | They propose to augment negative sampler into mixture distribution with adversarially learned sampler and to combine it with noise contrastive estimation (NCE) they observe faster convergence and improved results on multiple metrics. |
| Outcome: | The proposed model performs better on word embeddings, order embedds and knowledge graph embeddments and faster convergence and improved results on multiple metrics. |
Tailoring Instructions to Student’s Learning Levels Boosts Knowledge Distillation (2023.acl-long)
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| Challenge: | Recent success of natural language processing (NLP) is driven by the adoption of large-scale pretrained language models. |
| Approach: | They propose a method to determine the impact of distillation influence on student generalization ability by prioritizing samples likely to enhance the student's generalization abilities. |
| Outcome: | The proposed method outperforms 10 common knowledge distillation baselines on 6 text classification tasks in the GLUE benchmark. |
LTRS: Improving Word Sense Disambiguation via Learning to Rank Senses (2025.coling-main)
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| Challenge: | Conventional training strategies only consider predefined senses for target words and learn each of them from relatively limited instances, neglecting the influence of similar ones. |
| Approach: | They propose a method to rank senses to improve the task of word Sense Disambiguation (WSD) by ranking an expanded list of sense definitions. |
| Outcome: | The proposed method achieves a SOTA F1 score of 79.6% in Chinese WSD and shows faster convergence than previous methods. |
Learning to Collaborate for Question Answering and Asking (N18-1)
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| Challenge: | Question answering (QA) and question generation (QG) are closely related tasks. |
| Approach: | They propose a training algorithm that generalizes both Generative Adversarial Network and Generating Domain-Adaptive Nets under the question answering scenario. |
| Outcome: | The proposed training algorithm generalizes both Generative Adversarial Network (GAN) and Generating Domain-Adaptive Nets (GDAN) under the question answering scenario. |
Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data (2025.findings-naacl)
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Juanhui Li, Sreyashi Nag, Hui Liu, Xianfeng Tang, Sheikh Muhammad Sarwar, Limeng Cui, Hansu Gu, Suhang Wang, Qi He, Jiliang Tang
| Challenge: | Large Language Models (LLMs) have demonstrated superior language understanding abilities in many real-world NLP applications. |
| Approach: | They propose a learning-based sample selection method that incorporates signals from both teacher and student to enhance model performance. |
| Outcome: | The proposed method improves model performance across datasets with higher data efficiency. |
Responding E-commerce Product Questions via Exploiting QA Collections and Reviews (C18-1)
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| Challenge: | Existing QA and review collections can be used to provide instant responses to product questions . a proposed framework can be applied to a real-world commercial E-commerce site . |
| Approach: | They propose a framework for automatically responding product questions in E-commerce sites . existing QA pairs are exploited as distant supervision for learning to rank responses . |
| Outcome: | The proposed framework can return a ranked list of snippets serving as the automated response for a given question. |
Leveraging Relaxed Equilibrium by Lazy Transition for Sequence Modeling (2022.acl-long)
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| Challenge: | Using attention-based models, certain tokens are less ambiguous than others, and they require fewer refinements for disambiguation. |
| Approach: | They propose a lazy transition mechanism to adjust the significance of iterative refinements for each token representation. |
| Outcome: | The proposed model outperforms baseline models on several tasks with the same number of parameters. |
Doc2hash: Learning Discrete Latent variables for Documents Retrieval (N19-1)
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| Challenge: | Learning to hash via generative model is a powerful paradigm for fast similarity search in documents retrieval. |
| Approach: | They propose a method that trains a generative model to generate hash codes by using continuous relaxation on priors. |
| Outcome: | The proposed method outperforms other state-of-the-art methods in qualitative and quantitative experiments. |
Learning to Edit: Aligning LLMs with Knowledge Editing (2024.acl-long)
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Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang
| Challenge: | Existing knowledge editing techniques rely on memorizing updated knowledge, impeding LLMs from effectively combining the new knowledge with their inherent knowledge when answering questions. |
| Approach: | They propose a Learning to Edit framework that equips LLMs with the ability to apply updated knowledge to input questions through a two-phase process . |
| Outcome: | The proposed framework outperforms existing methods in knowledge editing tasks and compares it with four benchmarks and two LLM architectures. |
Zero-Shot Cross-Lingual Transfer with Meta Learning (2020.emnlp-main)
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| Challenge: | There are more than 7,000 languages spoken in the world, over 90 of which have more than 10 million native speakers each. |
| Approach: | They propose to use meta-learning to train a model on multiple languages at the same time . they use standard supervised, zero-shot cross-lingual, and few-shot crosses-lingual settings for different natural language understanding tasks. |
| Outcome: | The proposed setup improves on the state-of-the-art for a total of 15 languages. |
Continual Learning Using Only Large Language Model Prompting (2025.coling-main)
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| Challenge: | Existing continuous learning paradigms fine-tune language model parameters or use adapters or variants to adapt the LM. |
| Approach: | They propose a new continual learning paradigm wherein a large language model is regarded as a black box. |
| Outcome: | The proposed method outperforms baselines by a large margin in learning tasks incrementally. |
Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs (2025.findings-emnlp)
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Yunzhe Qi, Jinjin Tian, Tianci Liu, Ruirui Li, Tianxin Wei, Hui Liu, Xianfeng Tang, Monica Xiao Cheng, Jingrui He
| Challenge: | Learning to Instruct is a new paradigm for black-box LLMs with inaccessible internal states. |
| Approach: | They propose a new paradigm that formulates instruction optimization as an LLM fine-tuning objective for a white-box “instruction engineer” LLM. |
| Outcome: | The proposed framework outperforms strong baselines in performance and efficiency. |
Learning to Explain: Generating Stable Explanations Fast (2021.acl-long)
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| Challenge: | Existing methods for explaining outcome of machine learning models produce explanations, or rationales, which identify the attributions of features in an input example. |
| Approach: | They propose a Learning to Explain approach that learns the behaviour of an underlying explanation algorithm simultaneously from all training examples. |
| Outcome: | The proposed approach is 5 to 7.5104 times faster than existing models and has comparable faithfulness to the black-box model. |
Describe Me an Auklet: Generating Grounded Perceptual Category Descriptions (2023.emnlp-main)
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| Challenge: | Learning and using abstract perceptual concepts is under-investigated in the language-and-vision field. |
| Approach: | They propose a framework for testing category-level perceptual grounding in multi-modal language models by using separate neural networks to generate and interpret descriptions of visual categories. |
| Outcome: | The proposed framework compares prototype- and interpretation-based representations with the performance of the generation model and the interpretation model, which is an indicator of perceptual grounding. |
Learning to Plan by Updating Natural Language (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance in basic natural language tasks. |
| Approach: | They propose a method that iteratively updates the task plan with new steps and behavioral instructions to guide LLMs to generate the correct solutions step by step. |
| Outcome: | The proposed method improves performance on five reasoning type tasks and can be directly applied to other LLMs. |
Learning to Translate by Translating: Stabilizing the Dual Loop via Semantic-Aware Self-Evolution (2026.findings-acl)
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Kui Liu, Mingming Yin, Zuoli Tang, Zihao Li, Chilin Fu, Xiaolu Zhang, Jun Zhou, Lixin Zou, Chenliang Li
| Challenge: | Large Language Models (LLMs) have been successful in machine translation, but lack of high-quality parallel corpora and cost constrain scalability. |
| Approach: | They propose an LLM-driven dual-learning framework that enables autonomous translation . they employ a robust semantic-aware reward function that balances adequacy with reconstruction fidelity . |
| Outcome: | The proposed model outperforms larger models on benchmarks and achieves parity with state-of-the-art supervised baselines on mainstream benchmarks. |
A Study of Parameter Efficient Fine-tuning by Learning to Efficiently Fine-Tune (2024.findings-emnlp)
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| Challenge: | Existing methods for parameter-efficient fine-tuning (PEFT) are limited due to the need for increased computational resources. |
| Approach: | They propose a method to learn PEFT parameters from data by projecting high dimensional parameters onto low dimensional parameter manifolds or identifying PEFT parametrically. |
| Outcome: | The proposed method can be used to identify PEFT parameters on GLUE tasks. |
Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI (2026.acl-long)
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| Challenge: | Existing methods struggle with content-style entanglement, leading to poor generalization across domains. |
| Approach: | They propose an explanation-by-design framework that explicitly disentangles style from content through architectural separation-by design. |
| Outcome: | The proposed framework disentangles style from content through architectural separation-by-design. |