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Title: Graph Neural Networks with Generated Parameters for Relation Extraction

Paper content:
Recently, progress has been made towards improving relational reasoning in machine learning field.
Among existing models, graph neural networks (GNNs) is one of the most effective approaches for multi-hop relational reasoning.
In fact, multi-hop relational reasoning is indispensable in many natural language processing tasks such as relation extraction.
In this paper, we propose to generate the parameters of graph neural networks (GP-GNNs) according to natural language sentences, which enables GNNs to process relational reasoning on unstructured text inputs.
We verify GP-GNNs in relation extraction from text.
Experimental results on a human-annotated dataset and two distantly supervised datasets show that our model achieves significant improvements compared to the baselines.
We also perform a qualitative analysis to demonstrate that our model could discover more accurate relations by multi-hop relational reasoning.
Recent years, graph neural networks (GNNs) have been applied to various fields of machine learning, including node classification BID10 , relation classification BID22 , molecular property prediction BID6 , few-shot learning BID5 , and achieve promising results on these tasks.
These works have demonstrated GNNs' strong power to process relational reasoning on graphs.Relational reasoning aims to abstractly reason about entities/objects and their relations, which is an important part of human intelligence.
Besides graphs, relational reasoning is also of great importance in many natural language processing tasks such as question answering, relation extraction, summarization, etc.
Consider the example shown in Fig. 1 , existing relation extraction models could easily extract the facts that Luc Besson directed a film Léon: The Professional and that the film is in English, but fail to infer the relationship between Luc Besson and English without multi-hop relational reasoning.
By considering the reasoning patterns, one can discover that Luc Besson could speak English following a reasoning logic that Luc Besson directed Léon: The Professional and this film is in English indicates Luc Besson could speak English.
However, most existing GNNs can only process multi-hop relational reasoning on pre-defined graphs and cannot be directly applied in natural language relational reasoning.
Enabling multi-hop relational reasoning in natural languages remains an open problem.To address this issue, in this paper, we propose graph neural networks with generated parameters (GP-GNNs), to adapt graph neural networks to solve the natural language relational reasoning task.
GP-GNNs first constructs a fully-connected graph with the entities in the sequence of text.
After that, it employs three modules to process relational reasoning: (1) an encoding module which enables edges to encode rich information from natural languages, (2) a propagation module which propagates relational information among various nodes, and (3) a classification module which makes predictions with node representations.
As compared to traditional GNNs, GP-GNNs could learn edges' parameters from natural languages, extending it from performing inferring on only non-relational graphs or graphs with a limited number of edge types to unstructured inputs such as texts.In the experiments, we apply GP-GNNs to a classic natural language relational reasoning task: relation extraction from text.
We carry out experiments on Wikipedia corpus aligned with Wikidata knowledge base BID25 Figure 1 : An example of relation extraction from plain text.
Given a sentence with several entities marked, we model the interaction between these entities by generating the weights of graph neural networks.
Modeling the relationship between "Léon" and "English" as well as "Luc Besson" helps discover the relationship between "Luc Besson" and "English".model
outperforms other state-of-the-art models on relation extraction task by considering multihop relational reasoning. We also
perform a qualitative analysis which shows that our model could discover more relations by reasoning more robustly as compared to baseline models.Our main contributions are in two-fold:(1) We extend a novel graph neural network model with generated parameters, to enable relational message-passing with rich text information, which could be applied to process relational reasoning on unstructured inputs such as natural languages.(2) We verify
our GP-GNNs in the task of relation extraction from text, which demonstrates its ability on multi-hop relational reasoning as compared to those models which extract relationships separately. Moreover, we
also present three datasets, which could help future researchers compare their models in different settings.
We addressed the problem of utilizing GNNs to perform relational reasoning with natural languages.
Our proposed models, GP-GNNs, solves the relational message-passing task by encoding natural language as parameters and performing propagation from layer to layer.
Our model can also be considered as a more generic framework for graph generation problem with unstructured input other than text, e.g. images, videos, audios.
In this work, we demonstrate its effectiveness in predicting the relationship between entities in natural language and bag-level and show that by considering more hops in reasoning the performance of relation extraction could be significantly improved.

assistant: text: A graph neural network model with parameters generated from natural languages, which can perform multi-hop reasoning.