[Paper Review] Adaptive Convolutional Filter Generation for Natural Language Understanding.

Dinghan Shen, Martin Renqiang Min+2 more|arXiv (Cornell University)|Sep 25, 2017

Topic Modeling9 citations

TL;DR

This paper proposes an adaptive convolutional filter generation framework for natural language understanding, where a meta network dynamically generates input-specific filters, enhancing representational expressiveness beyond static filters. The method achieves state-of-the-art performance on document categorization and answer sentence selection tasks by modeling co-dependent sentence representations through a two-way feature abstraction mechanism.

ABSTRACT

Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP are not expressive enough, in the sense that all input sentences share the same learned (and static) set of filters. Motivated by this problem, we propose an adaptive convolutional filter generation framework for natural language understanding, by leveraging a meta network to generate input-aware filters. We further generalize our framework to model question-answer sentence pairs and propose an adaptive question answering (AdaQA) model; a novel two-way feature abstraction mechanism is introduced to encapsulate co-dependent sentence representations. We investigate the effectiveness of our framework on document categorization and answer sentence-selection tasks, achieving state-of-the-art performance on several benchmark datasets.

Motivation & Objective

• Address the limitation of static, shared convolutional filters in existing CNN models for NLP, which restrict representational expressiveness.
• Enable input-aware filter generation by introducing a meta network that tailors filters to individual input sentences.
• Extend the framework to model co-dependent representations in question-answer pairs through a two-way feature abstraction mechanism.
• Improve performance on natural language understanding tasks such as document categorization and answer sentence selection.
• Achieve state-of-the-art results on benchmark datasets by enhancing model expressiveness through adaptive filtering.

Proposed method

• Employ a meta network to generate convolutional filters that are specific to each input sentence, enabling dynamic adaptation rather than using fixed filters.
• Integrate the meta network into a CNN architecture to produce input-dependent filter weights during forward pass.
• Design a two-way feature abstraction mechanism that jointly models question and answer representations, capturing their co-dependency.
• Train the model end-to-end using standard backpropagation, with the meta network learning to generate filters based on input context.
• Utilize the adaptive filters to extract more discriminative features from text sequences, improving downstream task performance.
• Generalize the framework to question-answering tasks by adapting the two-way abstraction to model inter-sentence relationships.

Experimental results

Research questions

• RQ1 Can dynamic, input-aware convolutional filters improve the representational capacity of CNNs in NLP compared to static filters?
• RQ2 How does adaptive filter generation affect performance on document categorization and sentence selection tasks?
• RQ3 To what extent can a two-way feature abstraction mechanism enhance co-dependent modeling of question-answer pairs?
• RQ4 Does the proposed framework achieve state-of-the-art performance on standard NLP benchmarks?
• RQ5 Can the meta-network effectively generate filters that generalize across diverse input sentences while maintaining task-specific expressiveness?

Key findings

• The proposed adaptive filter generation framework achieves state-of-the-art performance on multiple benchmark datasets for document categorization.
• The model outperforms existing CNN-based approaches on answer sentence selection tasks by leveraging input-specific filters.
• The two-way feature abstraction mechanism effectively captures co-dependent representations between questions and answers, improving reasoning capacity.
• The use of a meta network to generate filters leads to more expressive and context-sensitive feature extraction than static filter learning.
• Empirical results demonstrate consistent improvements across multiple NLP tasks, validating the effectiveness of adaptive filtering.
• The framework generalizes well to both single-sentence and paired-sentence understanding tasks, showing broad applicability.