1,891 research outputs found
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks
in Natural LanguageProcessing (NLP). Although text inputs are typically
represented as a sequence of tokens, there isa rich variety of NLP problems
that can be best expressed with a graph structure. As a result, thereis a surge
of interests in developing new deep learning techniques on graphs for a large
numberof NLP tasks. In this survey, we present a comprehensive overview onGraph
Neural Networks(GNNs) for Natural Language Processing. We propose a new
taxonomy of GNNs for NLP, whichsystematically organizes existing research of
GNNs for NLP along three axes: graph construction,graph representation
learning, and graph based encoder-decoder models. We further introducea large
number of NLP applications that are exploiting the power of GNNs and summarize
thecorresponding benchmark datasets, evaluation metrics, and open-source codes.
Finally, we discussvarious outstanding challenges for making the full use of
GNNs for NLP as well as future researchdirections. To the best of our
knowledge, this is the first comprehensive overview of Graph NeuralNetworks for
Natural Language Processing.Comment: 127 page
Co-guiding for Multi-intent Spoken Language Understanding
Recent graph-based models for multi-intent SLU have obtained promising
results through modeling the guidance from the prediction of intents to the
decoding of slot filling. However, existing methods (1) only model the
unidirectional guidance from intent to slot, while there are bidirectional
inter-correlations between intent and slot; (2) adopt homogeneous graphs to
model the interactions between the slot semantics nodes and intent label nodes,
which limit the performance. In this paper, we propose a novel model termed
Co-guiding Net, which implements a two-stage framework achieving the mutual
guidances between the two tasks. In the first stage, the initial estimated
labels of both tasks are produced, and then they are leveraged in the second
stage to model the mutual guidances. Specifically, we propose two heterogeneous
graph attention networks working on the proposed two heterogeneous semantics
label graphs, which effectively represent the relations among the semantics
nodes and label nodes. Besides, we further propose Co-guiding-SCL Net, which
exploits the single-task and dual-task semantics contrastive relations. For the
first stage, we propose single-task supervised contrastive learning, and for
the second stage, we propose co-guiding supervised contrastive learning, which
considers the two tasks' mutual guidances in the contrastive learning
procedure. Experiment results on multi-intent SLU show that our model
outperforms existing models by a large margin, obtaining a relative improvement
of 21.3% over the previous best model on MixATIS dataset in overall accuracy.
We also evaluate our model on the zero-shot cross-lingual scenario and the
results show that our model can relatively improve the state-of-the-art model
by 33.5% on average in terms of overall accuracy for the total 9 languages.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI). arXiv admin note: substantial text overlap with
arXiv:2210.1037
MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter
To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE
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