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Title: Neural Message Passing for Multi-Label Classification

Paper content:
Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample.
Modeling the combinatorial label interactions in MLC has been a long-haul challenge.
Recurrent neural network (RNN) based encoder-decoder models have shown state-of-the-art performance for solving MLC.
However, the sequential nature of modeling label dependencies through an RNN limits its ability in parallel computation, predicting dense labels, and providing interpretable results.
In this paper, we propose Message Passing Encoder-Decoder (MPED) Networks,  aiming to provide fast, accurate, and interpretable MLC.
MPED networks model the joint prediction of labels by replacing all RNNs in the encoder-decoder architecture with message passing mechanisms and dispense with autoregressive inference entirely.
The proposed models are simple, fast, accurate, interpretable, and structure-agnostic (can be used on known or unknown structured data).
Experiments on seven real-world MLC datasets show the proposed models outperform autoregressive RNN models across five different metrics with a significant speedup during training and testing time.
Multi-label classification (MLC) is receiving increasing attention in tasks such as text categorization and image classification.
Accurate and scalable MLC methods are in urgent need for applications like assigning topics to web articles, classifying objects in an image, or identifying binding proteins on DNA.
The most common and straightforward MLC method is the binary relevance (BR) approach that considers multiple target labels independently BID0 .
However, in many MLC tasks there is a clear dependency structure among labels, which BR methods ignore.
Accordingly, probabilistic classifier chain (PCC) models were proposed to model label dependencies and formulate MLC in an autoregressive sequential prediction manner BID1 .
One notable work in the PCC category was from which implemented a classifier chain using a recurrent neural network (RNN) based sequence to sequence (Seq2Seq) architecture, Seq2Seq MLC.
This model uses an encoder RNN encoding elements of an input sequence, a decoder RNN predicting output labels one after another, and beam search that computes the probability of the next T predictions of labels and then chooses the proposal with the max combined probability.However, the main drawback of classifier chain models is that their inherently sequential nature precludes parallelization during training and inference.
This can be detrimental when there are a large number of positive labels as the classifier chain has to sequentially predict each label, and often requires beam search to obtain the optimal set.
Aside from time-cost disadvantages, PCC methods have several other drawbacks.
First, PCC methods require a defined ordering of labels for the sequential prediction, but MLC output labels are an unordered set, and the chosen order can lead to prediction instability .
Secondly, even if the optimal ordering is known, PCC methods struggle to accurately capture long-range dependencies among labels in cases where the number of positive labels is large (i.e., dense labels).
For example, the Delicious dataset has a median of 19 positive labels per sample, so it can be difficult to correctly predict the labels at the end of the prediction chain.
Lastly, many real-world applications prefer interpretable predictors.
For instance, in the task of predicting which proteins (labels) will bind to a DNA sequence based binding site, users care about how a prediction is made and how the interactions among labels influence the predictions 1 .Message
Passing Neural Networks (MPNNs) BID3 introduce a class of methods that model joint dependencies of variables using neural message passing rather than an explicit representation such as a probabilistic classifier chain. Message
passing allows for efficient inference by modelling conditional independence where the same local update procedure is applied iteratively to propagate information across variables. MPNNs provide
a flexible method for modeling multiple variables jointly which have no explicit ordering (and can be modified to incorporate an order, as explained in section 3). To handle the
drawbacks of BR and PCC methods, we propose a modified version of MPNNs for MLC by modeling interactions between labels using neural message passing.We introduce Message Passing Encoder-Decoder (MPED) Networks aiming to provide fast, accurate, and interpretable multi-label predictions. The key idea
is to replace RNNs and to rely on neural message passing entirely to draw global dependencies between input components, between labels and input components, and between labels. The proposed
MPED networks allow for significantly more parallelization in training and testing. The main contributions
of this paper are:• Novel approach for MLC. To the authors' best knowledge
, MPED is the first work using neural message passing for MLC.• Accurate MLC. Our model achieves
similar, or better
performance compared to the previous state of the art across five different MLC metrics. We validate our model on seven MLC datasets
which cover a wide spectrum of input data structure: sequences (English text, DNA), tabular (bag-of-words), and graph (drug molecules), as well as output label structure: unknown and graph.• Fast. Empirically our model achieves an average
1.7x speedup
over the autoregressive seq2seq MLC at training time and an average 5x speedup over its testing time.• Interpretable. Although deep-learning based systems have
widely been viewed
as "black boxes" due to their complexity, our attention based MPED models provide a straightforward way to explain label to label, input to label, and feature to feature dependencies.
In this work we present Message Passing Encoder-Decoder (MPED) Networks which achieve a significant speedup at close to the same performance as autoregressive models for MLC.
We open a new avenue of using neural message passing to model label dependencies in MLC tasks.
In addition, we show that our method is able to handle various input data types (sequence, tabular, graph), as well various output label structures (known vs unknown).
One of our future extensions is to adapt the current model to predict more dynamic outputs.
BID1 BID24

assistant: text: We propose Message Passing Encoder-Decode networks for a fast and accurate way of modelling label dependencies for multi-label classification.