ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs (1512.05193v4)

Abstract: How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence's representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNN achieves state-of-the-art performance on AS, PI and TE tasks.

Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

In the domain of NLP, modeling the interdependence between pairs of sentences is pivotal for tasks such as answer selection (AS), paraphrase identification (PI), and textual entailment (TE). Previous work has mostly failed to capture inter-sentence interactions effectively, opting instead for independent sentence representations or heavily relying on manually designed linguistic features. The paper "ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs" introduces the Attention-Based Convolutional Neural Network (ABCNN) to address these inadequacies. This essay delineates the paper's methodology, contributions, and empirical findings, focusing on the effectiveness and implications of the proposed ABCNN models.

Contributions and Methodology

The authors of the paper propose three distinct attention mechanisms within a convolutional neural network (CNN) framework, collectively labeled as ABCNN:

1. ABCNN-1: Integrates an attention mechanism directly into the convolution input. This mechanism involves constructing an attention feature matrix that reflects the relatedness between units (words or phrases) of the two input sentences.
2. ABCNN-2: Applies the attention mechanism in a pooling layer. Attention weights, derived post-convolution, modulate the convolution output to re-emphasize significant units.
3. ABCNN-3: Combines the previous two approaches to allow attention mechanisms to operate on both the convolution input and pooling layers, capturing finer-grained inter-sentence dependencies.

These architectures enable the extraction of more nuanced and powerful sentence representations compared to traditional CNNs by dynamically focusing on the mutual influences between sentence pairs across different granularity levels.

Empirical Evaluation

The paper evaluates the proposed ABCNN models on three benchmark datasets corresponding to AS, PI, and TE tasks:

1. Answer Selection (WikiQA): The ABCNN models outperform state-of-the-art baselines such as CNN-Cnt by notable margins. For instance, the best-performing ABCNN-3 model achieves a Mean Average Precision (MAP) of 0.6921 and a Mean Reciprocal Rank (MRR) of 0.7108.
2. Paraphrase Identification (MSRP): The ABCNN models match or surpass the performance of competitive baselines like the MPSSM-CNN and MF-TF-KLD, achieving an accuracy and F1 score of 78.9% and 84.8%, respectively.
3. Textual Entailment (SICK): Leveraging a hybrid approach involving both original and non-overlapping word transformations, the ABCNN models set new standards with accuracies up to 86.2%, significantly improving upon the previous best result of 84.6% held by the Illinois-LH system.

Implications and Future Work

The integration of attention mechanisms into CNNs holds substantial implications for NLP:

• Enhanced Representation: ABCNNs enable dynamic focusing on relevant parts of sentences, thereby generating more contextually enriched sentence representations. This enhancement is crucial for tasks requiring fine-grained semantic understanding.
• General Applicability: The proposed models demonstrate robustness across various tasks, indicating their generalizability to other NLP domains where sentence pair modeling is crucial.

Future directions could explore the application of attention-based CNNs in generative tasks like machine translation, which has predominantly witnessed success with attention-based LSTM models. Moreover, extending the depth of ABCNNs with more convolutional layers might capture even higher-level abstractions, especially as larger annotated datasets become available.

Conclusion

The paper advances the state-of-the-art in sentence pair modeling through the innovative integration of attention mechanisms with CNNs. The empirical results substantiate the superiority of the ABCNN models over existing techniques across multiple NLP tasks. This work not only reaffirms the centrality of attention in neural networks but also paves the way for future explorations in deep learning architectures for NLP.