aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model (1801.01641v2)

Abstract: As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers. To achieve good results, however, these models have been combined with additional features such as word overlap or BM25 scores. Without this combination, these models perform significantly worse than methods based on linguistic feature engineering. In this paper, we propose an attention based neural matching model for ranking short answer text. We adopt value-shared weighting scheme instead of position-shared weighting scheme for combining different matching signals and incorporate question term importance learning using question attention network. Using the popular benchmark TREC QA data, we show that the relatively simple aNMM model can significantly outperform other neural network models that have been used for the question answering task, and is competitive with models that are combined with additional features. When aNMM is combined with additional features, it outperforms all baselines.

An Analysis of "aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model"

The paper "aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model" introduces a novel approach to semantic matching in question answering (QA) systems. This approach focuses on leveraging deep learning architectures to outperform traditional feature engineering-based methods in ranking short answer texts. The proposed method, aNMM (attention-based Neural Matching Model), aims to address several limitations of existing deep learning techniques in QA tasks, particularly the reliance on additional linguistic features to achieve competitive performance.

Model Architecture and Innovations

The aNMM architecture presents two primary innovations:

1. Value-Shared Weighting Scheme: Unlike Convolutional Neural Networks (CNNs) traditionally used in QA tasks, which employ position-shared weights suited for spatial data, aNMM uses a value-shared weighting scheme. This approach is based on the premise that the critical aspect of semantic matching in text is capturing the strength of semantic similarities rather than their positions. Specifically, this model utilizes learned weights to encode how various levels of semantic matching signals should be combined, effectively modeling the nuanced dependencies between question and answer terms.
2. Question Attention Network: To effectively determine the importance of different question terms, aNMM incorporates a question attention network. By using a softmax gating function, the network assigns varying importance to question terms based on the context of the answer, thus enabling the model to dynamically focus on more pertinent terms during the matching process.

Experimental Evaluation

The effectiveness of the aNMM model was evaluated using the TREC QA dataset, a standard benchmark for answer re-ranking tasks. The results showcase that the aNMM model surpasses previous state-of-the-art methods, particularly neural network models that heavily rely on additional features such as word overlap and BM25 scores. Key findings from the experiments are as follows:

• The aNMM model, even without additional features, outperformed other deep learning models, including those based on CNNs and Long Short-Term Memory (LSTM) networks.
• When combined with simple additional features like the Query Likelihood (QL) score, aNMM improved its performance further, establishing a new state-of-the-art in several evaluation metrics such as Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR).
• The question attention network in aNMM effectively models term importance, learning more robust ranking signals compared to traditional methods like inverse document frequency (IDF).

Implications and Future Directions

The advantages demonstrated by the aNMM model underscore its potential in enhancing QA systems, with practical benefits in search engines and digital assistants where accurate answer ranking is crucial. By diminishing the reliance on handcrafted features and linguistic parsers, aNMM presents a more generalizable and efficient approach.

Future research directions may include further refinement of deep learning architectures for QA tasks by exploring additional neural network layers or alternative attention mechanisms. Additionally, expanding the evaluation to non-factoid QA datasets could offer insights into the model's applicability across varied question types. Exploring multi-language support without extensive feature engineering is another promising area that builds on the foundational work presented in this model.

In summary, the aNMM model represents a significant step forward in the application of deep learning to QA tasks, offering a robust framework that balances performance with the practicalities of feature engineering and resource reliance.