3 research outputs found
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment
Network embeddings, which learn low-dimensional representations for each
vertex in a large-scale network, have received considerable attention in recent
years. For a wide range of applications, vertices in a network are typically
accompanied by rich textual information such as user profiles, paper abstracts,
etc. We propose to incorporate semantic features into network embeddings by
matching important words between text sequences for all pairs of vertices. We
introduce a word-by-word alignment framework that measures the compatibility of
embeddings between word pairs, and then adaptively accumulates these alignment
features with a simple yet effective aggregation function. In experiments, we
evaluate the proposed framework on three real-world benchmarks for downstream
tasks, including link prediction and multi-label vertex classification. Results
demonstrate that our model outperforms state-of-the-art network embedding
methods by a large margin.Comment: To appear at EMNLP 201
Reasoning Over Semantic-Level Graph for Fact Checking
Fact checking is a challenging task because verifying the truthfulness of a
claim requires reasoning about multiple retrievable evidence. In this work, we
present a method suitable for reasoning about the semantic-level structure of
evidence. Unlike most previous works, which typically represent evidence
sentences with either string concatenation or fusing the features of isolated
evidence sentences, our approach operates on rich semantic structures of
evidence obtained by semantic role labeling. We propose two mechanisms to
exploit the structure of evidence while leveraging the advances of pre-trained
models like BERT, GPT or XLNet. Specifically, using XLNet as the backbone, we
first utilize the graph structure to re-define the relative distances of words,
with the intuition that semantically related words should have short distances.
Then, we adopt graph convolutional network and graph attention network to
propagate and aggregate information from neighboring nodes on the graph. We
evaluate our system on FEVER, a benchmark dataset for fact checking, and find
that rich structural information is helpful and both our graph-based mechanisms
improve the accuracy. Our model is the state-of-the-art system in terms of both
official evaluation metrics, namely claim verification accuracy and FEVER
score.Comment: 9page
Improving Textual Network Learning with Variational Homophilic Embeddings
The performance of many network learning applications crucially hinges on the
success of network embedding algorithms, which aim to encode rich network
information into low-dimensional vertex-based vector representations. This
paper considers a novel variational formulation of network embeddings, with
special focus on textual networks. Different from most existing methods that
optimize a discriminative objective, we introduce Variational Homophilic
Embedding (VHE), a fully generative model that learns network embeddings by
modeling the semantic (textual) information with a variational autoencoder,
while accounting for the structural (topology) information through a novel
homophilic prior design. Homophilic vertex embeddings encourage similar
embedding vectors for related (connected) vertices. The proposed VHE promises
better generalization for downstream tasks, robustness to incomplete
observations, and the ability to generalize to unseen vertices. Extensive
experiments on real-world networks, for multiple tasks, demonstrate that the
proposed method consistently achieves superior performance relative to
competing state-of-the-art approaches.Comment: Accepted to NeurIPS 201