Papers with DG
FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring (2025.acl-short)
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| Challenge: | Existing algorithms for teacher feedback generation are time-consuming and costly to generate manually. |
| Approach: | They propose a framework for generating teacher feedback using LLMs and humans . they construct three datasets that are time-consuming and costly to generate manually . results show that incorporating a small portion of DM leads to superior performance . |
| Outcome: | The proposed framework performs better on three datasets compared to human-generated feedback and LLM-generated datasets. |
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding (2024.findings-acl)
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| Challenge: | Recent studies show that large-scale models can generate unsupervised DG without expensive distractor annotations. |
| Approach: | They propose a dual task training framework that integrates pseudo distractors from LLMs and answer information as the objective target with a two-stage training process. |
| Outcome: | The proposed method surpasses GPT-3.5-turbo zero-shot performance with 200 fewer model parameters. |
Fine-grained Contrastive Learning for Definition Generation (2022.aacl-main)
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| Challenge: | Recent pre-trained transformer-based definition generation models lack effective representation learning to contain full semantic components of the given word, leading to under-specific definitions. |
| Approach: | They propose a novel contrastive learning method that encourages the model to capture more detailed semantic representations from the definition sequence encoding. |
| Outcome: | The proposed method could generate more specific definitions compared with state-of-the-art models. |
White-Box Multi-Objective Adversarial Attack on Dialogue Generation (2023.acl-long)
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| Challenge: | Pre-trained transformers are popular in state-of-the-art dialogue generation systems . however, they are vulnerable to adversarial samples crafted by small and imperceptible perturbations. |
| Approach: | They propose a multi-objective attack method that balances two objectives: generation accuracy and length. |
| Outcome: | The proposed method significantly degrades state-of-the-art DG models with a higher success rate than traditional accuracy-based methods. |
Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering (2022.emnlp-main)
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| Challenge: | Existing approaches to limit overfitting of training domains are rooted in this problem . domain generalization (DG) seeks to train models on a small number of source domains . |
| Approach: | They propose to use knowledge distillation to train models on a small number of source domains to maximize their zero-shot out-of-domain utility. |
| Outcome: | The proposed model learns its source domains better and has better out-of-domain generalization . the proposed model outperforms existing approaches that aim to limit overfitting . |
DagoBERT: Generating Derivational Morphology with a Pretrained Language Model (2020.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) generate derivationally complex words, but it is unclear what they learn about other aspects of language. |
| Approach: | They propose to use BERT to examine its derivational capabilities in different settings, from unmodified pretrained models to full finetuning. |
| Outcome: | The proposed model outperforms the state-of-the-art in derivation generation. |
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies. (2020.findings-emnlp)
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| Challenge: | Existing distractor generation methods are far from practical, and there are still room for improvement. |
| Approach: | They propose a distractor generation scheme with multi-tasking and negative answer training strategies for generating multiple distractors. |
| Outcome: | The proposed scheme improves the state-of-the-art results from 28.65 to 39.81 (BLEU 1 score) and generates multiple distractors shows strong distracting power for multiple choice questions. |
Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation (2021.naacl-main)
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| Challenge: | Existing definition generation methods take the source word as an indecomposable semantic unit, but in parataxis languages like Chinese, word meanings can be composed using the word formation process. |
| Approach: | They propose to use word formation features to enhance Definition Generation (DG) in Chinese to generate an explanatory text. |
| Outcome: | The proposed model enhances Definition Generation (DG) in Chinese by decomposing the word meaning into different semantic components. |
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration (2024.findings-acl)
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Han Cheng Yu, Yu An Shih, Kin Man Law, KaiYu Hsieh, Yu Chen Cheng, Hsin Chih Ho, Zih An Lin, Wen-Chuan Hsu, Yao-Chung Fan
| Challenge: | Existing LMs undergo task-agnostic pertaining, but task-specific pretraining has gained prominence. |
| Approach: | They propose retrieval augmented pretraining and task-specific pretraining for DG . they propose to refine language model pretraining to align it more closely with downstream task . |
| Outcome: | The proposed method improves the performance of multiple-choice questions by integrating knowledge graphs and language models. |
Morpheme Sense Disambiguation: A New Task Aiming for Understanding the Language at Character Level (2024.lrec-main)
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| Challenge: | Morphemes are a strong linguistic feature to capture lexical semantics, but lack of morpheme-informed resources and the expense of manual annotations hinder morphme-enhanced methods. |
| Approach: | They propose a task of Morpheme Sense Disambiguation with two subtasks in-text and in-word to generalize morpheme features on more tasks. |
| Outcome: | The proposed tasks are based on two morpheme-annotated datasets for Chinese . the best model yields a promising precision of 77.66% on in-text and 88.19% on in word . |
CANDICE: Agentic Causal Disentanglement with Class Conditional Knowledge Integration for Long Tailed Domain Generalization (2026.findings-acl)
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| Challenge: | Domain generalization and long-tailed (LT) learning models face two challenges . domain invariance often suppresses class-discriminative signals essential for long-tail recognition. |
| Approach: | They propose a framework that disentangles domain-invariant and class-discriminative features . they evaluate 10 diverse medical imaging datasets spanning four modalities . |
| Outcome: | The proposed framework achieves an average performance improvement of 10.3% across multi-domain and in-domain long-tailed tasks while preserving minority class performance. |