Papers with matching models
Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots (P18-2)
Copied to clipboard
| 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. |
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems (D19-1)
Copied to clipboard
| Challenge: | Existing studies focus on constructing a matching model with sophisticated neural architectures, but do little to how to effectively learn such architectures from data. |
| Approach: | They propose to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems by using four sampling strategies. |
| Outcome: | The proposed learning method improves the performance of matching models on two benchmarks with three matching models. |
Dialogue Response Selection with Hierarchical Curriculum Learning (2021.acl-long)
Copied to clipboard
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)
Copied to clipboard
| 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. |
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing text semantic matching models do not provide granularity for text comparison. |
| Approach: | They propose a simple yet effective training strategy for text semantic matching by disentangling keywords from intents. |
| Outcome: | The proposed approach achieves stable performance improvements against a wide range of models on three benchmarks. |
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems (P19-1)
Copied to clipboard
| 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. |
Do It Once: An Embarrassingly Simple Joint Matching Approach to Response Selection (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing matching models for response selection perform the independent matching (IM) approach. Existing models for matching only perform one match regardless of the number of options. |
| Approach: | They propose a joint matching approach which performs matching only once regardless of the number of options. |
| Outcome: | The proposed approach outperforms existing models and reduces training time by over half. |
FormulaSPIN: Self-Play Fine-Tuning for Natural Language to Spreadsheet Formula Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to writing formulas rely on static supervised data, which quickly saturates on limited annotations. |
| Approach: | They propose a self-play framework that breaks the ceiling of supervised fine-tuning by enabling iterative self-improvement without any additional data. |
| Outcome: | The proposed framework outperforms existing approaches to fine-tuning on static data while enabling iterative self-improvement without additional data. |
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection (2020.emnlp-main)
Copied to clipboard
| 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. |
Modeling and Solving Stable Matching under Probabilistic Preferences with Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown strong capability in understanding and simulating humans’ decisions, suggesting a new way to use LLMs as tools to study social systems. |
| Approach: | They propose a Hybrid GS–LLM matching method that integrates Gale–Shapley with probabilistic acceptance decisions. |
| Outcome: | The proposed method outperforms classical baselines in terms of stability and improves robustness under uncertainty. |