Idée - Information retrieval dialogue system - # Query-bag based pseudo relevance feedback for information-seeking conversations

Enhancing Information-Seeking Conversations with Query-Bag Pseudo Relevance Feedback

Q: How can the QB-PRF framework be extended to other types of conversational systems beyond information-seeking, such as open-domain chatbots or task-oriented dialogues

The QB-PRF framework can be extended to other types of conversational systems by adapting its components to suit the specific requirements of those systems. For open-domain chatbots, the QBS module can be modified to retrieve a broader range of queries from diverse sources to enhance the chatbot's understanding of various topics. The QBF module can be adjusted to fuse information from different sources, such as knowledge bases or external data, to enrich the responses generated by the chatbot. In task-oriented dialogues, the framework can focus on selecting query-bags that are relevant to the specific tasks at hand, improving the system's ability to provide accurate and contextually appropriate responses. By customizing the framework's modules and training data to align with the characteristics of different conversational systems, it can be effectively applied beyond information-seeking scenarios.

Q: What are the potential limitations of the contrastive learning approach used in the QBS module, and how could it be further improved to handle more diverse and noisy query-bag data

One potential limitation of the contrastive learning approach in the QBS module is its sensitivity to the quality of the training data. If the dataset used for contrastive learning contains noisy or irrelevant queries, it can impact the effectiveness of the model in selecting relevant query-bags. To address this limitation and improve the handling of diverse and noisy query-bag data, several strategies can be implemented. Data Cleaning: Prior to training the contrastive learning model, a thorough data cleaning process can be conducted to remove noisy or irrelevant queries from the dataset. This can help improve the quality of the training data and enhance the model's performance. Augmentation Techniques: Incorporating data augmentation techniques can help increase the diversity of the training data and expose the model to a wider range of query variations. Techniques such as synonym replacement, paraphrasing, or back-translation can be used to generate additional training examples. Regularization: Applying regularization techniques during training, such as dropout or weight decay, can help prevent overfitting and improve the model's generalization ability, making it more robust to noisy data. Ensemble Learning: Utilizing ensemble learning methods by training multiple contrastive learning models with different initializations or hyperparameters can help mitigate the impact of noisy data and enhance the model's overall performance. By implementing these strategies and fine-tuning the contrastive learning approach, the QBS module can be further improved to handle diverse and noisy query-bag data more effectively.

Q: Given the importance of query-bag quality and diversity, how could the QB-PRF framework be combined with other techniques, such as data augmentation or few-shot learning, to further enhance its performance in low-resource settings

In low-resource settings, enhancing the performance of the QB-PRF framework can be achieved by combining it with other techniques such as data augmentation and few-shot learning. These strategies can help improve the quality and diversity of query-bags, leading to more robust and accurate conversational systems. Here are some ways to integrate these techniques: Data Augmentation: By incorporating data augmentation techniques like synonym replacement, paraphrasing, or adding noise to the queries, the framework can generate additional training examples and expose the model to a wider range of query variations. This can help improve the diversity of the query-bags and enhance the model's ability to handle different query expressions. Few-Shot Learning: Introducing few-shot learning approaches can enable the framework to learn from limited labeled data by leveraging pre-trained models or meta-learning algorithms. By providing the model with a small number of examples for new tasks or domains, it can quickly adapt and generalize to unseen query-bag data, enhancing its performance in low-resource settings. Transfer Learning: Leveraging transfer learning techniques by fine-tuning the framework on a related task with more data can help improve its performance in low-resource settings. By transferring knowledge from a larger dataset or a pre-trained model, the framework can learn better representations and enhance its ability to handle diverse query-bag data. By combining the QB-PRF framework with data augmentation, few-shot learning, and transfer learning techniques, it can be further optimized to deliver superior performance in low-resource settings, where limited labeled data is available.

Concepts de base

The core message of this paper is to propose a Query-bag Pseudo Relevance Feedback (QB-PRF) framework that can effectively enhance the performance of information-seeking conversation systems. The framework includes a Query-bag Selection Module (QBS) to retrieve and select relevant queries to form a query-bag, and a Query-bag Fusion Module (QBF) to fuse the query-bag information with the original query to improve the query representation.

Résumé

This paper proposes a Query-bag Pseudo Relevance Feedback (QB-PRF) framework to enhance the performance of information-seeking conversation systems. The key components are:

Representation Learning: The authors leverage unsupervised pre-training methods, specifically Variational Auto-Encoder (VAE), to obtain distinctive sentence representations that can serve as supervision signals for the QBS module.
Query-bag Selection (QBS) Module: The QBS module employs contrastive learning to select relevant queries from the unlabeled corpus to form the query-bag, leveraging the representations learned from the pre-trained VAE.
Query-bag Fusion (QBF) Module: The QBF module utilizes a transformer-based network to fuse the mutual information between the original query and the selected query-bag, generating a refined query representation.
Matching Model: The refined query representation from the QBF module is then fed into any downstream matching model to enhance its performance on information-seeking conversation tasks.

The authors verify the effectiveness of the QB-PRF framework on two competitive pre-trained backbone models, BERT and GPT-2, across two benchmark datasets (Quora and LaiYe). The experimental results demonstrate that the proposed framework significantly outperforms strong baselines, highlighting the importance of leveraging query-bag information to improve query representation and overall system performance.

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arxiv.org

Stats

The authors report the following key statistics:

The LaiYe dataset has 425,310 training, 40,000 validation, and 40,000 test queries, with an average of 11.20 queries per query-bag.
The Quora dataset has 56,294 training, 5,536 validation, and 10,000 test queries, with an average of 8.43 queries per query-bag.

Citations

None.

Idées clés tirées de

Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations

by Xiaoqing Zha... à arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04272.pdf

Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations

Questions plus approfondies

How can the QB-PRF framework be extended to other types of conversational systems beyond information-seeking, such as open-domain chatbots or task-oriented dialogues

The QB-PRF framework can be extended to other types of conversational systems by adapting its components to suit the specific requirements of those systems. For open-domain chatbots, the QBS module can be modified to retrieve a broader range of queries from diverse sources to enhance the chatbot's understanding of various topics. The QBF module can be adjusted to fuse information from different sources, such as knowledge bases or external data, to enrich the responses generated by the chatbot. In task-oriented dialogues, the framework can focus on selecting query-bags that are relevant to the specific tasks at hand, improving the system's ability to provide accurate and contextually appropriate responses. By customizing the framework's modules and training data to align with the characteristics of different conversational systems, it can be effectively applied beyond information-seeking scenarios.

What are the potential limitations of the contrastive learning approach used in the QBS module, and how could it be further improved to handle more diverse and noisy query-bag data

One potential limitation of the contrastive learning approach in the QBS module is its sensitivity to the quality of the training data. If the dataset used for contrastive learning contains noisy or irrelevant queries, it can impact the effectiveness of the model in selecting relevant query-bags. To address this limitation and improve the handling of diverse and noisy query-bag data, several strategies can be implemented.

Data Cleaning: Prior to training the contrastive learning model, a thorough data cleaning process can be conducted to remove noisy or irrelevant queries from the dataset. This can help improve the quality of the training data and enhance the model's performance.

Augmentation Techniques: Incorporating data augmentation techniques can help increase the diversity of the training data and expose the model to a wider range of query variations. Techniques such as synonym replacement, paraphrasing, or back-translation can be used to generate additional training examples.

Regularization: Applying regularization techniques during training, such as dropout or weight decay, can help prevent overfitting and improve the model's generalization ability, making it more robust to noisy data.

Ensemble Learning: Utilizing ensemble learning methods by training multiple contrastive learning models with different initializations or hyperparameters can help mitigate the impact of noisy data and enhance the model's overall performance.

By implementing these strategies and fine-tuning the contrastive learning approach, the QBS module can be further improved to handle diverse and noisy query-bag data more effectively.

Given the importance of query-bag quality and diversity, how could the QB-PRF framework be combined with other techniques, such as data augmentation or few-shot learning, to further enhance its performance in low-resource settings

In low-resource settings, enhancing the performance of the QB-PRF framework can be achieved by combining it with other techniques such as data augmentation and few-shot learning. These strategies can help improve the quality and diversity of query-bags, leading to more robust and accurate conversational systems. Here are some ways to integrate these techniques:

Data Augmentation: By incorporating data augmentation techniques like synonym replacement, paraphrasing, or adding noise to the queries, the framework can generate additional training examples and expose the model to a wider range of query variations. This can help improve the diversity of the query-bags and enhance the model's ability to handle different query expressions.

Few-Shot Learning: Introducing few-shot learning approaches can enable the framework to learn from limited labeled data by leveraging pre-trained models or meta-learning algorithms. By providing the model with a small number of examples for new tasks or domains, it can quickly adapt and generalize to unseen query-bag data, enhancing its performance in low-resource settings.

Transfer Learning: Leveraging transfer learning techniques by fine-tuning the framework on a related task with more data can help improve its performance in low-resource settings. By transferring knowledge from a larger dataset or a pre-trained model, the framework can learn better representations and enhance its ability to handle diverse query-bag data.

By combining the QB-PRF framework with data augmentation, few-shot learning, and transfer learning techniques, it can be further optimized to deliver superior performance in low-resource settings, where limited labeled data is available.