M3: Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval

Core Concepts

M3 introduces an advanced recursive multi-hop dense sentence retrieval system using a novel multi-task mixed-objective approach, achieving state-of-the-art performance on the FEVER dataset.

Abstract

Abstract

Contrastive learning is effective but can lead to suboptimal retrieval performance.
M3 introduces a novel multi-task mixed-objective approach for dense text representation learning.

Introduction

Open-domain fact verification involves single or multi-hop evidence extraction.
Three-stage approach: retriever, sentence reranker, claim classifier.

Dense Text Retrieval

Neural network-based encoders improve dense retrieval over traditional IR methods.

Multi-hop Text Retrieval

Multi-hop retrieval crucial for complex question-answering and fact verification tasks.

Method

M3 uses iterative sentence-level retrieve-and-rerank scheme for evidence retrieval.

Experimental Setup

Evaluation metrics include recall@5, label accuracy, and FEVER score on the FEVER dataset.

Results

M3 outperforms existing models in both document and sentence-level recall on the FEVER dataset.

Analysis

Effect of multi-task learning, mixed-objective learning, and hybrid-ranking algorithm on system performance.

Stats

Our approach yields state-of-the-art performance on the FEVER dataset.
The top two negative examples are kept for training in our model.

Quotes

"In this paper, we introduce M3, an advanced recursive multi-hop dense sentence retrieval system designed for fact verification."
"Our approach yields state-of-the-art performance on a large-scale open-domain fact verification benchmark dataset, FEVER."

Key Insights Distilled From

M3

by Yang Bai,Ant... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14074.pdf

Deeper Inquiries

How can the use of Wikipedia as the sole source of evidence impact the reliability of fact-checking systems

Relying solely on Wikipedia as the sole source of evidence in fact-checking systems can have implications for reliability. While Wikipedia is a valuable resource with a vast amount of information, it is not immune to inaccuracies or biases. The content on Wikipedia can be edited by users, leading to potential errors, vandalism, or outdated information. Additionally, certain topics may have limited coverage or biased perspectives on the platform. As a result, fact-checking systems that exclusively rely on Wikipedia may inadvertently propagate misinformation if the information presented is inaccurate or incomplete.

What are potential limitations of relying solely on contrastive learning in information retrieval systems

One potential limitation of relying solely on contrastive learning in information retrieval systems is its narrow focus on distinguishing between relevant and non-relevant pairs of data based on similarity metrics. While contrastive learning has proven effective for representation learning in dense retrieval tasks, it may not capture more nuanced relationships within the data beyond simple pairwise distinctions. This approach could lead to suboptimal performance when dealing with complex semantic relationships or multi-faceted queries where multiple objectives are at play.

How might incorporating diverse datasets with different objectives enhance the robustness of dense text representation learning

Incorporating diverse datasets with different objectives can significantly enhance the robustness of dense text representation learning in several ways:

Improved Generalization: Training models across various datasets exposes them to a wider range of contexts and tasks, enabling better generalization capabilities.
Enhanced Feature Learning: Different objectives provide complementary signals for feature learning, allowing models to capture diverse aspects of text representations.
Robustness Against Bias: By training across diverse datasets with varying objectives, models are less likely to overfit specific patterns present in one dataset and more likely to learn generalized features.
Adaptability: Models trained using mixed-objective learning frameworks can adapt better to new tasks and domains due to their exposure to varied training signals.
Performance Boost: Combining datasets with different objectives can lead to improved model performance by leveraging the strengths of each dataset's objective function while mitigating individual weaknesses through ensemble-like approaches.

By incorporating diverse datasets into training regimes using mixed-objective learning frameworks like M3-DSR presented in the context above, researchers can create more versatile and robust dense text representation models capable of handling a wide array of real-world challenges effectively.