Convolutional Neural Network Architectures for Matching Natural Language Sentences (1503.03244v1)

Abstract: Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.

• The paper presents two novel CNN architectures, Arc-I and Arc-II, for effective natural language sentence matching.
• It employs convolution, pooling, and joint interaction mechanisms to capture both local dependencies and hierarchical structures.
• Empirical studies demonstrate Arc-II's superior performance on sentence completion, tweet-response matching, and paraphrase identification tasks.

Overview of Convolutional Neural Network Architectures for Matching Natural Language Sentences

The paper "Convolutional Neural Network Architectures for Matching Natural Language Sentences" by Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen introduces novel convolutional neural network (CNN) models designed specifically for the task of sentence matching. This work aims to address the fundamental problem of modeling the correspondence between linguistic objects in various NLP tasks. The proposed CNN architectures—referred to as Arc-I and Arc-II—show considerable promise in modeling the hierarchical structures of sentences and capturing rich interaction patterns at multiple levels of abstraction.

Convolutional Sentence Model

The paper begins by introducing a convolutional architecture tailored for sentence modeling. This model takes a sequence of word embeddings as input and processes sentence structures using layers of convolution and pooling, culminating in a fixed-length vector representation. Key components include local receptive fields and shared weights in convolution units, similar to their counterparts in vision and speech recognition.

Convolution and Pooling

The convolution layers operate on sliding windows of words, ensuring that each word sequence segment is processed to capture local dependencies. Following convolution, max-pooling layers reduce the dimensionality of the representation and filter undesirable compositions. This architecture is flexible across varying sentence lengths through mechanisms like all-zero padding and a gating function to handle boundary conditions.

Architectures for Sentence Matching

The paper proposes two primary architectures—Arc-I and Arc-II—for the task of sentence matching.

Arc-I: Sequential Sentence Representation

Arc-I follows a conventional approach where each sentence is individually processed through separate CNNs to obtain vector representations. These representations are then compared using a multi-layer perceptron (MLP). While effective, Arc-I has limitations due to its deferred interaction, where the two sentences are compared only after their representations have been independently formed.

Arc-II: Interaction-Based Representation

Arc-II addresses the limitations of Arc-I by integrating the interaction space between sentences earlier in the network. It jointly processes sliding windows from both sentences through a sequence of 1D and 2D convolutions and max-pooling layers. This architecture preserves the sequential information of both sentences and enables deeper interaction modeling. Arc-II's ability to capture low-level and high-level abstractions concurrently positions it as a superior architecture for sentence matching tasks.

Empirical Study

The efficacy of the proposed models is validated across three distinct tasks:

1. Sentence Completion: Using a dataset derived from Reuters, the task involves predicting the second clause of a sentence given the first clause. Both Arc-I and Arc-II outperform traditional models and other deep learning-based approaches, with Arc-II showing the best performance.
2. Tweet-Response Matching: Using data from Weibo, the task involves matching tweets to their responses. Arc-II again demonstrates superior performance, leveraging its ability to model local matching patterns effectively.
3. Paraphrase Identification: The task of determining whether two sentences are paraphrases is tested using the Microsoft Research Paraphrase Corpus (MSRP). Although Arc-II shows competitive results, it does not surpass state-of-the-art methods that incorporate specialized features for paraphrase detection.

Implications and Future Work

The results underscore the advantages of convolutional approaches in capturing the structural and semantic nuances of sentences. Arc-II's superior performance, particularly in tasks involving sentence interactions, highlights its potential for broader applications in NLP tasks requiring fine-grained matching capabilities. Future research could focus on combining Arc-II with advanced training strategies, such as curriculum learning, to further exploit its capabilities. Additionally, there is room for exploring the synergistic effects of fine-tuning word embeddings within the proposed architectures to enhance performance on specific tasks.

In summary, the convolutional neural network architectures introduced in this paper offer significant advancements in the domain of natural language sentence matching. Their ability to model hierarchical sentence structures and rich interaction patterns sets a robust foundation for continued research and application in NLP.