Low-resource Contrastive Co-training for Generative Conversational Query Rewrite

Core Concepts

Utilizing contrastive co-training with data augmentation improves generative conversational query rewrite under low-resource settings.

Abstract

The article discusses the challenges of generative conversational query rewrite, focusing on noise and language style shift. It introduces a co-training paradigm with a Simplifier and Rewriter model, leveraging contrastive learning for better performance. Extensive experiments show the model's superiority in both few-shot and zero-shot scenarios. The use of weakly labeled data and contrastive learning enhances the model's generalization ability when facing different writing styles.

Stats

Recently, few-shot learning is gaining popularity for this task. Extensive experiments demonstrate the superiority of the model under both few-shot and zero-shot scenarios. The results show that performance can still be improved when the unlabeled dataset is large enough. Our model achieves the best overall scores on all metrics concerning both datasets.

Quotes

"We study low-resource generative conversational query rewrite that is robust to both noise and language style shift." "Our model leverages dual models (Rewriter and Simplifier) through pseudo-labeling for enhancing each other iteratively." "Extensive experiments demonstrate the effectiveness and superior generalization ability of CO3."

Key Insights Distilled From

CO3

by Yifei Yuan,C... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11873.pdf

Deeper Inquiries

How does the use of contrastive learning impact the model's performance in handling noise

Contrastive learning plays a crucial role in enhancing the model's performance in handling noise. By using contrastive learning, the model can focus on distinguishing valuable information from noise in the input data. This technique helps the model learn common semantic patterns between similar inputs and differentiate differences between dissimilar ones. As a result, the model becomes more robust to noise present in the data, leading to improved performance by reducing the impact of noisy input queries.

What are potential limitations or drawbacks of relying on weakly labeled data for training

While weakly labeled data can be beneficial for training models under low-resource settings, there are potential limitations and drawbacks associated with relying solely on such data. One limitation is that weakly labeled data may not provide accurate or high-quality annotations compared to gold-labeled data. This could lead to inconsistencies or errors in training the model, affecting its overall performance and generalization ability. Additionally, depending too heavily on weakly labeled data may limit the diversity and richness of information available for training, potentially hindering the model's capacity to learn complex patterns effectively.

How can this co-training paradigm be applied to other NLP tasks beyond generative conversational query rewrite

The co-training paradigm used for generative conversational query rewrite can be applied to various other NLP tasks beyond this specific domain. The concept of co-training involves training multiple models simultaneously where each provides guidance for enhancing the other iteratively through pseudo-labeling and contrastive learning techniques. This approach can be adapted to tasks like text summarization, sentiment analysis, machine translation, named entity recognition (NER), and more. By leveraging unlabeled data effectively and incorporating dual-model interactions with iterative improvements, this co-training framework has broad applicability across different NLP domains requiring robust language understanding capabilities.