Extreme Multi-Label Classification with Dual-Encoders

핵심 개념

Dual-encoder models can outperform SOTA methods in extreme multi-label classification tasks by using decoupled softmax loss and soft top-k operator-based loss.

초록

The content discusses the use of dual-encoder models for extreme multi-label classification tasks. It highlights the limitations of existing contrastive losses and proposes new loss functions to improve performance. The study includes experiments on synthetic datasets and large benchmarks, showcasing the effectiveness of the proposed approach.

Directory:

1. Abstract
   • DE models are effective in retrieval tasks but underexplored in XMC.
   • Proposed decoupled softmax loss and soft top-k operator-based loss.
2. Introduction
   • DE models for openQA systems.
   • XMC scenarios require memorization and generalization.
3. Background: Multi-Label Classification
   • Definition of query-document relevance distribution.
   • Description of DE models and classification networks.
4. Improved Training of Dual-Encoder Models
   • Limitations of standard contrastive losses for XMC problems.
   • Proposal of DecoupledSoftmax loss and SoftTop-k operator-based loss.
5. Experiments
   • Comparison with existing XMC methods on various datasets.
6. Conclusions & Limitations

통계

Current empirical evidence indicates that DE models fall significantly short on XMC benchmarks, where SOTA methods linearly scale the number of learnable parameters with the total number of classes (documents in the corpus) by employing per-class classification head.
When trained with proposed loss functions, standard DE models alone can match or outperform SOTA methods by up to 2% at Precision@1 even on the largest XMC datasets while being 20× smaller in terms of trainable parameters.

인용구

"Our work shows that pure DE models can indeed match or even outperform SOTA XMC methods by up to 2% even on the largest public XMC benchmarks while being 20× smaller in model size."

핵심 통찰 요약

Dual-Encoders for Extreme Multi-Label Classification

by Nilesh Gupta... 게시일 arxiv.org 03-19-2024

https://arxiv.org/pdf/2310.10636.pdf

더 깊은 질문

How can the proposed decoupled softmax loss be applied to other machine learning tasks beyond extreme multi-label classification

提案されたデカップルドソフトマックス損失は、極端なマルチラベル分類以外の機械学習タスクにどのように適用できるでしょうか？
提案されたDecoupledSoftmax損失関数は、他の多くの機械学習タスクにも適用可能です。例えば、画像認識や自然言語処理などの領域では、畳み込みニューラルネットワーク（CNN）やリカレントニューラルネットワーク（RNN）と組み合わせて使用することが考えられます。これらのタスクでは、特徴量を抽出して共通空間に埋め込んだり、複数の入力から最も重要な情報を取得したりする必要があります。DecoupledSoftmaxはこのような場面で正確な予測を行いつつ、不均衡なデータセットでも効果的に訓練することが期待されます。

What potential challenges could arise when implementing these new loss functions in real-world applications

新しいロス関数を実際のアプリケーションに実装する際に生じる可能性がある課題は何ですか？
新しいロス関数を実世界アプリケーションに導入する際、いくつかの課題が考えられます。まず第一に、計算コストとメモリ使用量が増加する可能性があります。特に大規模なデータセットや高次元特徴空間では計算負荷が増すため、効率的な実装方法や十分なリソース確保が必要です。また、新しいロス関数を導入することで既存システムとの互換性やパフォーマンスへの影響も考慮しなければなりません。さらに、過学習や収束速度への影響も注意深く評価する必要があります。

How might advancements in dual-encoder models impact other areas of machine learning research

デュアルエンコーダーモデルの進歩は他の機械学習分野へどう影響しますか？
デュアルエンコーダーモデル技術は他の多くの機械学習分野へ革新的な影響を与える可能性があります。例えば自然言語処理（NLP）では文書生成や質問応答システム向上へ利用される見込みです。画像処理領域能でも物体認識精度向上や異常値検知等で活用される見通しがあります。
Dual-Encoders for Extreme Multi-Label Classification
Published as a conference paper at ICLR 2024
DUAL-ENCODERS FOR EXTREME MULTI-LABEL
CLASSIFICATION
Nilesh Gupta†⋄∗ Devvrit Khatri†⋄
Ankit Singh Rawat‡
Srinadh Bhojanapalli‡
Prateek Jain‡
Inderjit Dhillon†⋄
The University of Texas at Austin ⋄Google ‡Google Research ABSTRACT Dual-encoder (DE) models are widely used in retrieval tasks, most commonly stud-
ied on open QA benchmarks that are often characterized by multi-class and limited training data. In contrast, their performance in multi-label and data-rich retrieval settings like extreme multi-label classification (XMC), remains under-explored. Current empirical evidence indicates that DE models fall significantly short on XMC benchmarks, where SOTA methods (Dahiya et al., 2023a;b) linearly scale the number of learnable parameters with the total number of classes (documents in the corpus) by employing per-class classification head. To this end, we first study and highlight that existing multi-label contrastive training losses are not appropriate for training DE models on XMC tasks. We propose decoupled softmax loss – a simple modification to the InfoNCE loss – that overcomes the limitations of existing contrastive losses. We further extend our loss design to a soft top-k operator-based loss which is tailored to optimize top-k prediction performance. When trained with our proposed loss functions, standard DE models alone can match or outperform SOTA methods by up to 2% at Precision@1 even on the largest XMC datasets while being 20× smaller in terms of...
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How can proposed decoupled softmax loss be applied other machine learning tasks beyond extreme multi label classification?
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