Papers with learning method
Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference (2020.starsem-1)
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| Challenge: | a logic-based approach to Natural Language Inference is becoming less and less common . a new method uses semantic relations to abduct sentences from data . |
| Approach: | They propose a method to reverse a theorem-proving procedure to abduct semantic relations from data. |
| Outcome: | The proposed method improves the performance of the theorem prover on the SICK dataset by 1.4% while maintaining high precision (>94%) |
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification (2022.naacl-main)
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| Challenge: | Existing few-shot text classification methods often lack labeled data in real-world tasks. |
| Approach: | They propose a meta-learning method that encodes how to attend for given tasks . they evaluate the method on five benchmark datasets and show it is competitive . |
| Outcome: | The proposed method performs better on five benchmark datasets than previous methods on labeled data. |
Measuring and Improving Semantic Diversity of Dialogue Generation (2022.findings-emnlp)
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| Challenge: | Existing evaluation metrics for response diversity do not capture the semantic diversity of generated responses. |
| Approach: | They propose an automatic evaluation metric to measure the semantic diversity of generated responses . they show that it captures human judgments better than existing diversity metrics . |
| Outcome: | The proposed metric captures human judgments on response diversity better than existing lexical diversity metrics. |
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems (N18-1)
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| Challenge: | Existing methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. |
| Approach: | They propose a hybrid imitation and reinforcement learning method that integrates user feedback and reinforcement training to improve the agent's performance. |
| Outcome: | The proposed method can learn from the mistake it makes via imitation learning from user teaching and feedback. |
Multilingual Collaborative Defense for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing safeguards for Large Language Models are vulnerable to "jailbreaking" harmful queries. |
| Approach: | They propose a learning method that optimizes a continuous soft safety prompt automatically to facilitate multilingual safeguarding of LLMs. |
| Outcome: | The proposed method outperforms previous approaches in multilingual jailbreak defense while exhibiting strong cross-lingual generalization. |
Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Aspect-based sentiment analysis (ABSA) identifies sentiment information related to specific aspects . previous studies have proposed using fixed examples for instruction tuning . |
| Approach: | They propose an instruction learning method with retrieval-based example ranking for ABSA tasks. |
| Outcome: | The proposed method is superior to existing models on three ABSA subtasks. |
Meta-Transfer Learning for Code-Switched Speech Recognition (2020.acl-main)
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| Challenge: | Increasing number of people in the world today speak a mixed-language as a result of being multilingual. |
| Approach: | They propose a method to transfer learn on a code-switched speech recognition system by extracting information from high-resource monolingual datasets. |
| Outcome: | The proposed model outperforms baselines on speech recognition and language modeling tasks and is faster to converge. |
Trial and Error: Exploration-Based Trajectory Optimization of LLM Agents (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have become integral components in various autonomous agent systems. |
| Approach: | They propose an exploration-based trajectory optimization approach that allows agents to learn from their exploration failures. |
| Outcome: | The proposed method outperforms baseline methods on three complex tasks by a large margin. |
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization (2021.naacl-main)
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| Challenge: | State-of-the-art abstractive summarization models rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available. |
| Approach: | They propose to use domain adaptation methods to simulate the low-resource domain adaptation setting for abstractive summarization systems with existing datasets across six diverse target domains. |
| Outcome: | The proposed model can be used to adapt to a low-resource domain adaptation setting. |
Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion (2021.emnlp-main)
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| Challenge: | Existing methods for entity set expansion define the expansion boundary using seed-based distance metrics, which are hard to adjust due to the extremely sparse supervision. |
| Approach: | They propose a new learning method for bootstrapping which jointly models the bootstraping process and boundary learning process in a GAN framework. |
| Outcome: | The proposed method achieves the new state-of-the-art performance for entity set expansion. |
SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)
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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li
| Challenge: | Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy. |
| Approach: | They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration. |
| Outcome: | The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent . |