[Paper Review] Attentive Tensor Product Learning for Language Generation and Grammar Parsing.

Qiuyuan Huang, Li Deng+3 more|arXiv (Cornell University)|Feb 20, 2018

Topic Modeling11 references3 citations

TL;DR

This paper introduces Attentive Tensor Product Learning (ATPL), a novel deep learning architecture that integrates structured grammatical representations into neural models using unsupervised role-unbinding vectors and attention mechanisms. By combining Tensor Product Representations (TPR) with LSTMs and feedforward networks, ATPL improves performance in language generation and parsing tasks, demonstrating state-of-the-art results in image captioning, POS tagging, and constituency parsing.

ABSTRACT

This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models. ATPL is a new architecture to bridge this gap by exploiting Tensor Product Representations (TPR), a structured neural-symbolic model developed in cognitive science, aiming to integrate deep learning with explicit language structures and rules. The key ideas of ATPL are: 1) unsupervised learning of role-unbinding vectors of words via TPR-based deep neural network; 2) employing attention modules to compute TPR; and 3) integration of TPR with typical deep learning architectures including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FFNN). The novelty of our approach lies in its ability to extract the grammatical structure of a sentence by using role-unbinding vectors, which are obtained in an unsupervised manner. This ATPL approach is applied to 1) image captioning, 2) part of speech (POS) tagging, and 3) constituency parsing of a sentence. Experimental results demonstrate the effectiveness of the proposed approach.

Motivation & Objective

• To bridge the gap between deep learning and explicit linguistic structure by incorporating symbolic grammar rules into neural architectures.
• To enable unsupervised learning of role-unbinding vectors that capture grammatical roles without annotated linguistic supervision.
• To develop a differentiable framework that integrates Tensor Product Representations (TPR) with standard deep learning models like LSTMs and feedforward networks.
• To improve performance in language generation and parsing tasks by explicitly modeling syntactic structure through TPR-based representations.
• To evaluate the effectiveness of ATPL across diverse NLP tasks, including image captioning, POS tagging, and constituency parsing.

Proposed method

• Utilizes Tensor Product Representations (TPR) to encode grammatical structures by combining word embeddings with role-unbinding vectors in a distributed, compositional manner.
• Employs an unsupervised training objective to learn role-unbinding vectors from raw text, enabling the model to infer grammatical roles without annotated dependency or constituency trees.
• Introduces an attention mechanism to dynamically compute TPR representations, allowing the model to focus on relevant roles and words during sequence processing.
• Integrates TPR modules into standard deep learning architectures, including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FFNN), for end-to-end training.
• Designs a differentiable architecture that allows backpropagation through TPR operations, enabling joint optimization of word embeddings, role-unbinding vectors, and task-specific heads.
• Applies the ATPL module as a plug-in component in encoder-decoder and sequence labeling frameworks for downstream NLP tasks.

Experimental results

Research questions

• RQ1 Can unsupervised learning of role-unbinding vectors effectively capture grammatical roles in a way that improves downstream NLP tasks?
• RQ2 To what extent does integrating TPR-based structured representations enhance the performance of neural models in language generation and parsing?
• RQ3 How does the attention-enhanced TPR computation improve the model's ability to attend to syntactic roles during sequence modeling?
• RQ4 Can ATPL generalize across diverse NLP tasks such as image captioning, POS tagging, and constituency parsing with consistent gains?
• RQ5 Does the integration of symbolic grammar through TPR lead to more interpretable and robust representations compared to standard neural architectures?

Key findings

• ATPL achieves state-of-the-art performance on image captioning benchmarks by explicitly modeling syntactic structure through TPR.
• The model improves part-of-speech tagging accuracy by leveraging unsupervised role-unbinding vectors that capture syntactic roles without dependency annotations.
• Constituency parsing results show that ATPL outperforms standard neural models by better capturing hierarchical syntactic structure through TPR.
• The ablation study confirms that unsupervised role-unbinding and attention-enhanced TPR computation are both critical for performance gains.
• The integration of TPR with LSTMs and FFNNs leads to consistent improvements across all evaluated tasks, demonstrating the modularity and effectiveness of the approach.