A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations (1511.08277v1)

Abstract: Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this problem, we present a new deep architecture to match two sentences with multiple positional sentence representations. Specifically, each positional sentence representation is a sentence representation at this position, generated by a bidirectional long short term memory (Bi-LSTM). The matching score is finally produced by aggregating interactions between these different positional sentence representations, through $k$-Max pooling and a multi-layer perceptron. Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations: An Overview

Semantic matching is a critical task in numerous natural language processing domains such as question answering and information retrieval. The paper "A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations" introduces an advanced neural network model, MV-LSTM, that enhances sentence matching performance by employing multiple positional sentence representations. This approach aims to overcome the limitations of previous models that compress entire sentences into single representation vectors, thus potentially losing vital local context.

Core Contributions

The paper's main contributions span the development of a robust model framework that emphasizes capturing and leveraging contextualized local information:

1. Multiple Positional Sentence Representations: Unlike previous models relying on single or multiple granularity sentence representations, MV-LSTM introduces positional sentence representations. Each representation is generated using bidirectional long short-term memory networks (Bi-LSTM), which capture the local and holistic meaning of sentences at varying positions.
2. Complex Interaction Modeling: The model integrates three interaction functions—cosine similarity, bilinear, and tensor layer—to compare positional representations. These functions allow MV-LSTM to model complex interaction patterns between sentence pairs effectively.
3. Top-k Interaction Aggregation: By employing a $k$-Max pooling strategy, the model extracts the most significant interaction signals, enhancing the capability to capture key sentence matching attributes that contribute to the final matching score.

Experimental Validation

The authors validate the effectiveness of the MV-LSTM on tasks such as question answering and sentence completion. The experiments reveal that MV-LSTM significantly outperforms existing models such as ARC-I, LSTM-RNN, and MultiGranCNN, demonstrating improvements across metrics like Precision at 1 (P@1) and Mean Reciprocal Rank (MRR).

• On the question answering task, MV-LSTM with a tensor layer achieves a P@1 of 0.766 and an MRR of 0.869, showcasing the model's superior capability to appropriately weight multiple positional sentence interactions.
• Similarly, in sentence completion, MV-LSTM achieves a P@1 of 0.691, indicating strong performance gains over traditional baselines.

Implications and Future Directions

The ability of MV-LSTM to leverage multiple positional sentence representations addresses a critical gap in traditional semantic matching methods by maintaining a more nuanced understanding of sentence structures. Additionally, its approach to contextualize local interactions could inspire future research exploring improved semantic representations in even more complex NLP tasks.

Future developments could explore integrating MV-LSTM with other advanced LLMs or extending its application to multi-sentence or document-level semantic matching. Additionally, optimizing the computational efficiency of such complex models could make them more viable for real-time applications.

In conclusion, MV-LSTM represents a significant advancement in neural semantic matching models, offering a refined method for capturing intricate textual relationships without sacrificing crucial contextual information. This research provides a valuable framework for both theoretical exploration and practical implementation of advanced semantic models, with ongoing potential for enhancement and application across diverse NLP challenges.