Extreme Multi-label Text Classification

Presented By

Mohan Wu

Introduction

In this paper, the authors are interested a field of problems called extreme classification. These problems involve training a classifier to give the most relevant tags for any given text; the difficulties arises from the fact that the label set is so large that most models give poor results. The authors propose a new model called APLC-XLNet which fine tunes the generalized autoregressive pretrained model (XLNet) by using Adaptive Probabilistic Label Clusters (APLC) to calculate cross entropy loss. This method takes advantage of unbalanced label distributions by forming clusters to reduce training time. The authors experimented on five different datasets and achieved results far better than existing state-of-the-art models.

Motivation

Extreme multi-label text classification (XMTC) has applications in many recent problems such as providing word representations of a large vocabulary [1], tagging Wikipedia with relevant labels [2] and giving product descriptions for search advertisements [3]. The authors are motivated by the shortcomings of traditional methods in the creation of XMTC. For example, one such method of classifying text is the bag-of-words (BOW) approach where a vector represents the frequency of a word in a corpus. However, BOW does not consider the location of the words so it cannot determine context and semantics. Motivated by the success of transfer learning in a wide range of natural language processing (NLP) problems, the authors propose to adapt XLNet [4] on the XMTC problem. The final challenge is the nature of the labelling distribution can be very sparse for some labels. The authors solve this problem by combining the Probabilistic Label Tree [5] method and the Adaptive Softmax [6] to create APLC.

Related Work

Two approaches have been proposed to solve the XMTC problem: traditional BOW techniques, and modern deep learning models.

BOW Approaches

Intuitively, we can apply the one-vs-all approach in which we can fit a classifier for each label and thus XMTC reduces to a binary classification problem. However, due to the large number of labels, this approach is very computationally expensive. There have been some techniques to reduce the complexity by pruning weights to induce sparsity but still this method is quite expensive. Another approach is to simply apply a dimensional reduction technique on the label space. However doing so have shown to have serious negative effects on the prediction accuracy. Finally, another approach is to use a tree to partition labels into groups based on similarity. This approach have shown to be quite fast but unfortunately, due to the problems with BOW methods, the accuracy is poor.

Deep Learning Approaches

Unlike BOW approaches, deep learning can learn dense representations of the corpus using context and semantics. One such example is X-BERT[7] which divides XTMC problems into 3 steps. First, it partitions the label set into clusters based on similarity. Next, it fits a BERT model on the label clusters for the given corpus. Finally, it trains simple linear classifiers to rank the labels in each cluster. Since there are elements of traditional techniques in X-BERT, namely, the clustering step and the linear classifier step, the authors propose to improve upon this approach.

APLC-XLNet

APLC-XLNet consists of three parts: the pretrained XLNet-Base as the base, the APLC output layer and a fully connected hidden layer connecting the pool layer of XLNet the output layer. One major challenge in XTMC problems is that most data fall into a small group of labels. To tackle this challenge, the authors propose partitioning the label set into one head cluster, [math]\displaystyle{ V_h }[/math], and many tail clusters, [math]\displaystyle{ V_1 \cup \ldots \cup V_K }[/math]. The head cluster contains the most popular labels while the tail clusters contains the rest of the labels. The clusters are then inserted in a 2-level tree where the root node is the head cluster and the leaves are the tail clusters. Using this architecture improves computation times significantly since most of the time the data stops at the root node.

The authors define the probability of each label as follows:

\begin{equation} p(y_{ij} | x) = \begin{cases} p(y_{ij}|x) & \text{if } y_{ij} \in V_h \\ p(V_t|x)p(y_{ij}|V_t,x) & \text{if } y_{ij} \in V_t \end{cases} \end{equation}

where [math]\displaystyle{ x }[/math] is the feature of a given sample, [math]\displaystyle{ y_{ij} }[/math] is the j-th label in the i-th sample, and [math]\displaystyle{ V_t }[/math] is the t-th tail cluster. Let [math]\displaystyle{ Y_i }[/math] be the set of labels for the i-th sample, and define [math]\displaystyle{ L_i = |Y_i| }[/math]. The authors propose an intuitive objective loss function for multi-label classification:

\begin{equation} J(\theta) = -\frac{1}{\sum_{i=1}^N L_i} \sum_{i=1}^N \sum_{j \in Y_i} (y_{ij} logp(y_{ij}) + (1 - y_{ij}) log(1- p(y_{ij})) \end{equation} where [math]\displaystyle{ N }[/math] is the number of samples, [math]\displaystyle{ p(y_{ij}) }[/math] is defined above and [math]\displaystyle{ y_{ij} \in \{0, 1\} }[/math].

The number of parameters in this model is given by: \begin{equation} N_{par} = d(l_h + K) + \sum_{i=1}^K \frac{d}{q^i}(d+l_i) \end{equation} where [math]\displaystyle{ d }[/math] is the dimension of the hidden state of [math]\displaystyle{ V_h }[/math], [math]\displaystyle{ q }[/math] is a decay variable, [math]\displaystyle{ l_h = |V_h| }[/math] and [math]\displaystyle{ l_i = |V_i| }[/math]. Furthermore, the computational cost can be expressed as follows: \begin{align} C &= C_h + \sum_{i=1}^K C_i \\ &= O(N_b d(l_h + K)) + O(\sum_{i=1}^K p_i N_b \frac{d}{q^i}(l_i + d)) \end{align} where [math]\displaystyle{ N_b }[/math] is the batch size.

Training APLC-XLNet

Training APLC-XLNet essentially boils down to training its three parts. The authors suggest using discriminative fine-tuning method[8] to train the model entirely while assigning different learning rates to each part. Since XLNet is pretrained, the learning rate, [math]\displaystyle{ \eta_x }[/math], should be small, while the output layer is specific to this type of problem so its learning rate, [math]\displaystyle{ \eta_a }[/math], should be large. For the connecting hidden layer, the authors chose a learning rate, [math]\displaystyle{ \eta_h }[/math], such that [math]\displaystyle{ \eta_x \lt \eta_h \lt \eta_a }[/math]. For each of the learning rates, the authors suggest a slanted triangular learning schedule defined as: \begin{equation} \eta = \begin{cases} \eta_0 \frac{t}{t_w} & \text{if } t \leq t_w \\ \eta_0 \frac{t_a - t}{t_a - t_w} & \text{if } t > t_w \end{cases} \end{equation} where [math]\displaystyle{ \eta_0 }[/math] is the starting learning rate, [math]\displaystyle{ t }[/math] is the current step, [math]\displaystyle{ t_w }[/math] is the chosen warm-up threshold and [math]\displaystyle{ t_a }[/math] is the total number of steps.

Results

The authors tested the APLC-XLNet model in several benchmark datasets against current state-of-the-art models. The evaluation metric, P@k is defined as: \begin{equation} P@k = \frac{1}{k} \sum_{i \in rank_k(\hat{y})} y_i \end{equation} where [math]\displaystyle{ rank_k(\hat{y}) }[/math] is the top k ranked probability in the prediction vector, [math]\displaystyle{ \hat{y} }[/math].

To help with tuning the model in the number of clusters and different partitions, the authors experimented on two different datasets: EURLex and Wiki10.

The three different partitions in the second graph the authors used were (0.7, 0.2, 0.1), (0.33, 0.33, 0.34), and (0.1, 0.2, 0.7) where 3 clusters were fixed.

Conclusion

The authors have proposed a new deep learning approach to solve the XMTC problem based on XLNet, namely, APLC-XLNet. Furthermore, the authors have provided a framework for other models that could use APLC as the output layer. Their experiments show that APLC-XLNet has better results in several benchmark datasets over the current state-of-the-art models.

References

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