Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling (1703.04826v4)

Abstract: Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-the-art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.

• The paper introduces a novel method that integrates graph convolutional networks with syntactic dependency structures to enhance semantic role labeling.
• It employs an edge-wise gating mechanism to dynamically weight syntactic dependencies, yielding significant performance gains on the CoNLL-2009 benchmark.
• The model combines LSTMs with GCNs to capture both sequential and non-linear syntactic relationships, resulting in robust and adaptable NLP performance.

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

This paper introduces a novel approach for the task of Semantic Role Labeling (SRL) by leveraging Graph Convolutional Networks (GCNs) to model syntactic dependency graphs. Semantic Role Labeling, a key step in NLP, involves identifying the predicate-argument structure of a given sentence. The authors Diego Marcheggiani and Ivan Titov propose using GCNs to enhance sentence encodings by integrating syntactic information, achieving state-of-the-art results on the CoNLL-2009 benchmark for both English and Chinese.

Methodology

Graph Convolutional Networks: The core of this approach centers on GCNs, which build upon traditional convolutional neural networks but extend their application to graph-structured data. For SRL, the nodes of these graphs represent words in a sentence, and the edges illustrate the syntactic dependencies among them. Instead of treating sentences as linear sequences, GCNs enable capturing richer and non-linear syntactic structures.

Incorporating Syntax in GCNs: The authors extend the standard GCN model to accommodate syntactic dependency trees, which are directed and labeled graphs. They introduce label-specific GCN parameters and allow information propagation in both directions of the dependency arcs (i.e., from head to dependent and vice versa). This is particularly important for languages with varied syntactic structures like Chinese.

Edge-wise Gating Mechanism: Given the reliance on automatically predicted syntactic dependencies, the model introduces edge-wise gates to dynamically weigh the importance of each edge in the graph. This mechanism helps mitigate the impact of erroneous syntactic parses by down-weighting less relevant edges.

Complementarity of GCNs and LSTMs: The combination of GCNs with Long Short-Term Memory (LSTM) networks proves crucial. While LSTMs capture sequential information efficiently, they may struggle with long-range dependencies critical for SRL. The GCNs provide a complementary role by incorporating immediate syntactic neighborhood information, thereby enhancing overall model performance.

Experimental Results

Dataset and Setup: The authors validate their model on the CoNLL-2009 dataset for both English and Chinese. The benchmarks involve identifying arguments and labeling them with semantic roles, given predefined predicates in the sentences. The model's hyperparameters were fine-tuned using the English development set, ensuring optimal performance.

Performance Evaluation: The model incorporating one GCN layer (K=1) on top of a BiLSTM encoder achieves significant improvements over syntax-agnostic models. Specifically, the authors report a 0.6% boost in F1 score for English and a notable 1.9% for Chinese, highlighting the effectiveness of integrating syntactic information through GCNs. The inclusion of edge-wise gates further enhances the model's performance, underscoring the importance of dynamically weighting syntactic edges.

Comparative Analysis: On the standard SRL benchmarks, the proposed model outperforms state-of-the-art systems, both local and global, in terms of F1 score. For English, the model achieves 88.0% F1 score in the local setting and 89.1% with a 3-model ensemble, marking a substantial enhancement over previous best results. Notably, the model also demonstrates robust performance on out-of-domain data, surpassing previous syntax-aware models and highlighting its adaptability to varying domains.

Implications and Future Directions

Practical Implications: The integration of GCNs into the SRL pipeline provides a robust method for leveraging syntactic representations, enhancing the performance of downstream NLP tasks like information extraction and question answering. The ability to handle long-range dependencies effectively is particularly beneficial for complex linguistic structures.

Theoretical Implications: The findings affirm the complementary nature of GCNs and LSTMs. While LSTMs capture sequential dependencies, GCNs add a layer of syntactic understanding, leading to a more holistic representation of sentence structures. This dual mechanism paves the way for future research to explore deeper integrations of graph-based models with sequence models in NLP.

Future Developments: The authors suggest extending the use of GCNs to other NLP tasks that can benefit from syntactic information, such as machine translation and discourse parsing. Exploring multi-layer GCNs and integrating global modeling techniques could further refine the SRL framework. Additionally, employing more sophisticated gating mechanisms or alternative graph representations may unlock new potentials in handling linguistic nuances.

The method developed in this paper serves as a significant advancement in the incorporation of syntactic dependencies in neural models, showcasing the potential for broader application in various language technologies.