Attention-over-Attention Neural Networks for Reading Comprehension (1607.04423v4)

Abstract: Cloze-style queries are representative problems in reading comprehension. Over the past few months, we have seen much progress that utilizing neural network approach to solve Cloze-style questions. In this paper, we present a novel model called attention-over-attention reader for the Cloze-style reading comprehension task. Our model aims to place another attention mechanism over the document-level attention, and induces "attended attention" for final predictions. Unlike the previous works, our neural network model requires less pre-defined hyper-parameters and uses an elegant architecture for modeling. Experimental results show that the proposed attention-over-attention model significantly outperforms various state-of-the-art systems by a large margin in public datasets, such as CNN and Children's Book Test datasets.

• The paper introduces the attention-over-attention model which bolsters traditional mechanisms by applying an extra layer over document-level attention.
• It leverages bi-directional GRUs and pair-wise matching to capture rich contextual relationships, achieving superior accuracy on CNN/Daily Mail and CBTest datasets.
• The model’s straightforward design and effective N-best re-ranking yield a notable 2.0% improvement over previous systems, indicating strong potential for broader NLP applications.

Attention-over-Attention Neural Networks for Reading Comprehension

The paper "Attention-over-Attention Neural Networks for Reading Comprehension" presents a novel architecture aimed at enhancing machine performance on cloze-style reading comprehension tasks. The research introduces the attention-over-attention (AoA) model, which applies an additional attention mechanism over document-level attention to produce more accurate predictions. Significant improvements in performance are highlighted, surpassing existing state-of-the-art systems on benchmark datasets.

Overview of Cloze-style Reading Comprehension

Cloze-style reading comprehension involves predicting a missing word in a sentence, considering context from a provided document. Traditionally, datasets such as the CNN/Daily Mail and Children's Book Test (CBTest) datasets have been used to evaluate models' ability to infer relationships between context and query. Within these datasets, the task is formulated as a triple $$\langle \mathcal{D}, \mathcal{Q}, \mathcal{A} \rangle$$ consisting of a document, a query, and the answer.

The Proposed AoA Model

The AoA model enhances conventional attention mechanisms by introducing an attention-over-attention mechanism. This structure places another layer of attention over the document-level attention, generating 'attended attention' for selecting the final answer. The significance of this approach lies in its simplicity and effectiveness, eschewing complex architectures in favor of leveraging learned weights to combine individual document-level attentions.

Model Components and Functionality

1. Contextual Embedding: Words in documents and queries are represented as continuous embeddings, processed by bi-directional GRUs to capture context.
2. Pair-wise Matching: A matrix of pair-wise matching scores is computed, reflecting the similarity between document and query words.
3. Attention Mechanisms: Document-level attention is calculated for each query word, and a row-wise softmax generates query-level attentions. The dot product of these attentions results in the 'attention-over-attention', providing a more focused attention over the document for prediction.
4. Final Predictions and Training: The model predicts answers through a sum attention approach, maximizing log-likelihood of the correct answers during training.

Numerical Results and Performance

Experimental results demonstrate that the AoA model significantly surpasses previous approaches on the CNN/Daily Mail and CBTest NE/CN datasets. For instance, on the CBTest NE test set, a notable improvement of 2.0% was achieved compared to the previous best model. The inclusion of an N-best re-ranking strategy further bolsters these results, effectively mimicking human validation of candidate answers by reevaluating their suitability within query contexts.

Implications and Future Directions

The proposed methodology underscores the utility of refining attention mechanisms to incorporate mutual information from both the document and query, highlighting the AoA model's superior handling of long documents and varying frequencies of candidate answers. The results suggest promising potential applications in other NLP tasks that require deep semantic understanding between paired inputs.

Future investigations could explore extending the AoA framework to broader NLP applications, ensuring that neural networks do more than act as advanced LLMs but truly 'comprehend' and reason out presented information. Another avenue could involve developing more intricate reasoning capabilities in systems to tackle sophisticated multi-sentence inference tasks.

In summary, the AoA model presents an effective enhancement to attention-based neural networks for reading comprehension, achieving state-of-the-art performance with a streamlined approach that holds significant potential for future NLP research and applications.