Comparative Study of CNN and RNN for Natural Language Processing (1702.01923v1)

Abstract: Deep neural networks (DNN) have revolutionized the field of NLP. Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle various NLP tasks. CNN is supposed to be good at extracting position-invariant features and RNN at modeling units in sequence. The state of the art on many NLP tasks often switches due to the battle between CNNs and RNNs. This work is the first systematic comparison of CNN and RNN on a wide range of representative NLP tasks, aiming to give basic guidance for DNN selection.

• The paper provides a detailed comparison showing that CNNs excel at capturing local features while RNNs deliver superior semantic understanding.
• The study emphasizes that hyperparameters such as hidden and batch sizes significantly affect performance across diverse NLP tasks.
• The findings suggest that combining CNN and RNN architectures could enhance model performance in practical NLP applications.

Comparative Study of CNN and RNN for Natural Language Processing

The paper "Comparative Study of CNN and RNN for Natural Language Processing" by Wenpeng Yin, Katharina Kann, Mo Yu, and Hinrich Schütze systematically explores the performance of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) on a diverse set of NLP tasks. This extensive investigation reveals the strengths and weaknesses of each architectural paradigm, providing crucial insights into DNN selection for various NLP applications.

Key Findings

1. Complementary Strengths:
   • The paper indicates that CNNs and RNNs serve complementary roles in text classification tasks. Specifically, CNNs excel in scenarios that require capturing position-invariant features, where local key phrases drive the sentiment or classification decision. Conversely, RNNs, particularly those incorporating gating mechanisms like GRUs and LSTMs, offer superior performance when the task necessitates a deeper semantic understanding of the entire sequence.
2. Hyperparameter Sensitivity:
   • The research highlights that model performance is significantly influenced by hyperparameters such as hidden size and batch size. While learning rate adjustments tend to produce relatively smooth performance changes, hidden and batch sizes can induce substantial performance variability. This underscores the importance of careful hyperparameter tuning for achieving optimal results with both CNNs and RNNs.

Experimental Setup and Results

The paper categorizes the NLP tasks into four groups: Text Classification (TextC), Semantic Matching (SemMatch), Sequence Order (SeqOrder), and Context Dependency (ContextDep). Each category is represented by a specific set of tasks:

• TextC:
  • Sentiment Classification (SentiC) and Relation Classification (RC).
  • GRU achieves the highest accuracy in SentiC, demonstrating its efficacy in capturing global semantics. In RC, the performance of CNN and GRU is comparably robust.
• SemMatch:
  • Textual Entailment (TE), Answer Selection (AS), and Question Relation Matching (QRM).
  • RNN-based models, particularly GRU, show superior performance in TE, a task demanding understanding of full sentence semantics. On the other hand, CNNs outperform in AS and QRM, likely due to their proficiency in key-phrase identification and local pattern recognition.
• SeqOrder:
  • Path Query Answering (PQA).
  • Both GRU and LSTM models outperform CNNs, given their capacity to handle order-dependent sequences effectively.
• ContextDep:
  • Part-of-Speech (POS) Tagging.
  • Bi-directional RNNs surpass CNNs and uni-directional RNNs, leveraging their ability to consider the context from both directions.

Qualitative Analysis

The paper provides a detailed error analysis of the sentiment classification task, illustrating scenarios where CNNs and GRUs either succeed or fail. GRUs outperform in cases requiring the interpretation of long-range contextual dependencies, while CNNs exhibit strength in identifying sentiment from salient local features. This analysis reinforces the conclusion that the nature of the task, specifically its dependency on global versus local features, should guide the choice of neural network architecture.

Implications and Future Work

The findings presented offer significant implications for both theoretical understanding and practical application in NLP. Given the complementary strengths of CNNs and RNNs, hybrid models or ensemble approaches could be explored to leverage the advantages of both architectures. Moreover, the pronounced impact of hyperparameters on performance emphasizes the need for rigorous tuning and possibly automated hyperparameter optimization techniques.

Future research may further delineate the boundary conditions under which each architecture excels by integrating more complex language tasks and diversified datasets. Additionally, exploring the integration of attention mechanisms with CNNs and RNNs could provide new avenues for enhancing model performance across various NLP challenges.

In summary, this paper provides a comprehensive comparison of CNN and RNN models across a spectrum of NLP tasks. It elucidates the conditions favoring each architecture and stresses the critical role of hyperparameter tuning in achieving maximum model efficacy. These insights not only aid in making informed decisions regarding DNN selection but also pave the way for future innovations in NLP model architecture.