Papers with matching model
Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition (2024.findings-eacl)
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| Challenge: | Techniques, Tactics and Procedures (TTPs) mapping is a difficult task for CTI extraction . conventional learning approaches target the problem in the classical multiclass/label classification setting . |
| Approach: | They propose a neural matching architecture that incorporates a sampling-based learn-to-compare mechanism to facilitate the learning process. |
| Outcome: | The proposed model reduces the complexity of competing over large label space. |
Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots (P18-2)
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| Challenge: | Existing methods to learn matching models for retrieval-based chatbots are lacking. |
| Approach: | They propose a method that uses a sequence-to-sequence architecture model as a weak annotator to judge the matching degree of unlabeled pairs and performs learning with both the weak signals and the unlabed data. |
| Outcome: | The proposed method improves on two public data sets on matching models on retrieval-based chatbots. |
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning (2021.naacl-main)
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| Challenge: | Existing zero-shot learning methods for multi-label text classification mostly learn a matching model between the feature space of text and the label space. |
| Approach: | They propose to use a graph encoder to incorporate label hierarchies to learn effective label representations on the zero-shot multi-label text classification problem. |
| Outcome: | The proposed approach outperforms previous non-pretrained methods on the zero-shot multi-label text classification task. |
Dialogue Response Selection with Hierarchical Curriculum Learning (2021.acl-long)
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Yixuan Su, Deng Cai, Qingyu Zhou, Zibo Lin, Simon Baker, Yunbo Cao, Shuming Shi, Nigel Collier, Yan Wang
| Challenge: | Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics. |
| Approach: | They propose a hierarchical curriculum learning framework that trains matching models in an “easy-to-difficult” scheme. |
| Outcome: | The proposed framework significantly improves the model performance across evaluation metrics on three benchmark datasets with three state-of-the-art matching models. |
Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains (2020.emnlp-main)
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| Challenge: | Asymmetrical text matching is a fundamental problem in information retrieval and natural language processing. |
| Approach: | They propose a method that regularizes features vectors projected from different domains . WD-Match can be used to improve different text matching methods . |
| Outcome: | The proposed method outperforms existing methods and benchmarks on four datasets. |
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems (P19-1)
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| Challenge: | Existing methods for learning a robust matching model from noisy training data are retrieval-based or generation-based. |
| Approach: | They propose a general co-teaching framework that learns matching models from noisy training data. |
| Outcome: | The proposed learning framework can improve existing models on two public data sets. |
DISK: Domain-constrained Instance Sketch for Math Word Problem Generation (2022.coling-1)
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| Challenge: | Existing methods for generating MWP text from equations are inflexible and require pre-defined templates. |
| Approach: | They propose a neural model which generates MWPs from equations by constructing a Quantity Cell Graph from the retrieved MWp instance and reasoning over it. |
| Outcome: | The proposed model performs impressively on educational MWP set and on human evaluation metrics. |
Extractive Summarization as Text Matching (2020.acl-main)
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| Challenge: | Currently, most of the neural extractive summarization systems score and extract sentences individually and model the relationship between sentences. |
| Approach: | They propose to instantiate a neural extractive summarization task as a semantic text matching problem and use it to match a source document and candidate summaries in a semantic space. |
| Outcome: | The proposed framework is faster and more efficient than existing frameworks. |
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection (2020.emnlp-main)
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| Challenge: | Existing approaches to learning-to-rank response selection are suboptimal due to ignorance of diversity of response quality. |
| Approach: | They propose to use off-the-shelf response retrieval models as automatic grayscale data generators to train response selection models. |
| Outcome: | The proposed approach can be automated without human effort on grayscale data. |