Papers with learning tasks
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing (D19-61)
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| Challenge: | Existing methods to train supervised learning models rely on labeled data, which is expensive or impossible to acquire. |
| Approach: | They propose an inductive transfer learning method that can augment learning models by infusing similar instances from different learning tasks in Natural Language Processing domain. |
| Outcome: | The proposed method improves the performance of three major news classification datasets by reducing dependency on labeled data by a significant margin. |
OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following (2024.acl-long)
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| Challenge: | Embodied Instruction Following (EIF) is a crucial task in embodied learning . however, there is n'a unified understanding regarding the impact of various components on task performance . |
| Approach: | They propose a framework that delineates the core components essential for embodied learning tasks . they integrate a multi-agent design into the Planner component of their LLM-centric architecture . |
| Outcome: | OPEx delineates the core components essential for solving embodied learning tasks . integrating a multi-agent design into the Planner component of the LLM-centric architecture further elevates performance. |
Zero-shot Medical Entity Retrieval without Annotation: Learning From Rich Knowledge Graph Semantics (2021.findings-acl)
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| Challenge: | Current approaches to medical entity retrieval generalize poorly to unseen sub-specialties . zero-shot retrieval is challenging due to the high degree of ambiguity and variability in medical corpora . |
| Approach: | They propose a set of learning tasks designed to train efficient zero-shot entity retrieval models. |
| Outcome: | The proposed architecture outperforms common zero-shot benchmarks with 7% to 30% higher recall across multiple major medical ontologies. |
Manifold-Preserving Transformers are Effective for Short-Long Range Encoding (2023.findings-emnlp)
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| Challenge: | Multi-head self-attention-based Transformers have shown promise in different learning tasks . but encoders of Transformers and their variants fail to preserve layer-wise contextual information . |
| Approach: | They propose an encoder model that guarantees a theoretical bound for layer-wise distance preservation between a pair of tokens. |
| Outcome: | The proposed model preserves equivalence between tokens and performs better than Transformers. |
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use (2024.emnlp-main)
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| Challenge: | Existing methods for embodied agents to learn and perform tasks use low-level instructions, which may not reflect natural human communication. |
| Approach: | They propose to use different types of language inputs to facilitate reinforcement learning (RL) embodied agents. |
| Outcome: | The proposed methods show that agents trained with diverse and informative language can achieve enhanced generalization and fast adaptation to new tasks in an open world. |
MOTIF: Contextualized Images for Complex Words to Improve Human Reading (2022.lrec-1)
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| Challenge: | Existing studies have shown that multimodal information is crucial for concept formation, accordingly for language acquisition. |
| Approach: | They collect a multimodal dataset enriched with complex word annotations and validated image match. |
| Outcome: | The proposed dataset contains 1125 comprehension texts retrieved from Wikipedia Simple Corpus . |
Semi-Supervised Lifelong Language Learning (2022.findings-emnlp)
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Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Yongbin Li, Nevin L. Zhang
| Challenge: | Existing methods to learn languages only focus on supervised learning, and unlabeled data is underexplored. |
| Approach: | They propose a semi-supervised lifelong language learning setting where a model learns sequentially arriving language tasks with both labeled and unlabeled data. |
| Outcome: | The proposed model outperforms baseline models on various language tasks and is effective and superior to existing models. |
Distill and Replay for Continual Language Learning (2020.coling-main)
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| Challenge: | Existing models fail to isolate acquired knowledge and forget previously learned tasks when learning in a stream where data distribution may shift. |
| Approach: | They propose a framework that distills knowledge and replays experience from previous tasks when fitting on a new task. |
| Outcome: | The proposed framework outperforms state-of-the-art models in continuously learning tasks of the same type but from different domains, as well as tasks of different types. |
R2D2: Robust Data-to-Text with Replacement Detection (2022.emnlp-main)
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| Challenge: | Existing methods to mitigate unfaithful text generation are inadequate . data-to-text generation requires a structured input format . |
| Approach: | They propose a training framework that addresses unfaithful Data-to-Text generation by training a system as a generator and faithfulness discriminator with additional replacement detection and unlikelihood learning tasks. |
| Outcome: | The proposed training framework improves FeTaQA, LogicNLG, and ToTTo fidelity on D2T systems. |
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective (2022.emnlp-main)
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Ping Yang, Junjie Wang, Ruyi Gan, Xinyu Zhu, Lin Zhang, Ziwei Wu, Xinyu Gao, Jiaxing Zhang, Tetsuya Sakai
| Challenge: | Existing approaches to zero-shot learning are format-agnostic and can address new learning tasks without additional training. |
| Approach: | They propose a new paradigm for zero-shot learning that is format agnostic and compatible with any format and applicable to a list of language tasks. |
| Outcome: | The proposed model shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as text classification and commonsense reasoning. |
PerCQA: Persian Community Question Answering Dataset (2022.lrec-1)
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| Challenge: | Community Question Answering (CQA) forums provide answers to many real-life questions. |
| Approach: | They propose to make Persian dataset PerCQA public to encourage more research in Persian CQA. |
| Outcome: | The proposed dataset contains 989 questions and 21,915 annotated answers from the most well-known Persian forum. |
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale (2024.findings-acl)
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| Challenge: | Multi-task learning (MTL) has shown significant practical benefits when using language models . current two stage MTL introduces a substantial number of additional parameters . |
| Approach: | They propose a multi-task learning method that leverages existing knowledge for a target task. |
| Outcome: | The proposed method outperforms baselines on three benchmarks and two encoder LMs with a small number of transfer parameters. |
Position: LLMs Can be Good Tutors in English Education (2025.emnlp-main)
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Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, Qingsong Wen
| Challenge: | Recent efforts to integrate large language models into English education lack adaptability to language learning. |
| Approach: | They argue that large language models can be effective tutors in English education . they encourage interdisciplinary research to explore these roles, fostering innovation and risks . |
| Outcome: | The proposed models can play three critical roles: 1) as data enhancers, 2) as task predictors, 3) as agents, enabling personalized and inclusive education. |