GILE: A Generalized Input-Label Embedding for Text Classification

GILE: A Generalized Input-Label Embedding for Text Classification Nikolaos Pappas James Henderson Idiap Research Institute, Martigny 1920, Switzerland {nikolaos.pappas,james.henderson@idiap.ch} Abstract Neural text classification models typically treat output labels as categorical variables that lack description and semantics. This forces their parametrization to be dependent on the label set size, and, hence, they are unable to scale to large label sets and generalize to unseen ones. Existing joint input-label text models overcome these issues by exploiting label descriptions, but they are unable to capture complex label relationships, have rigid parametrization, and their gains on unseen labels happen often at the expense of weak performance on the labels seen during training. In this paper, we propose a new input-label model that generalizes over previous such models, addresses their limitations, and does not compromise performance on seen labels. The model consists of a joint nonlinear input- label embedding with controllable capacity and a joint-space-dependent classification unit that is trained with cross-entropy loss to opti- mize classification performance. We evaluate models on full-resource and low- or zero- resource text classification of multilingual news and biomedical text with a large label set. Our model outperforms monolingual and multilingual models that do not leverage label semantics and previous joint input-label space models in both scenarios. 1 Introduction Text classification is a fundamental NLP task with numerous real-world applications such as topic recognition (Tang et al., 2015; Yang et al., 2016), sentiment analysis (Pang and Lee, 2005; Yang et al., 2016), and question answering (Chen et al., 2015; Kumar et al., 2015). Classification also appears as a sub-task for sequence prediction tasks such as neural machine translation (Cho et al., 2014; Luong et al., 2015) and summarization (Rush et al., 2015). Despite numerous studies, existing models are trained on a fixed label set using k-hot vectors, and therefore treat target labels as mere atomic symbols without any particular structure to the space of labels, ignoring potential linguistic knowledge about the words used to describe the output labels. Given that semantic representations of words have been shown to be useful for representing the input, it is reasonable to expect that they are going to be useful for representing the labels as well. Previous work has leveraged knowledge from the label texts through a joint input-label space, initially for image classification (Weston et al., 2011; Mensink et al., 2012; Frome et al., 2013; Socher et al., 2013). Such models generalize to labels both seen and unseen during training, and scale well on very large label sets. However, as we explain in Section 2, existing input-label models for text (Yazdani and Henderson, 2015; Nam et al., 2016) have the following limitations: (i) their embedding does not capture complex label relationships due to its bilinear form, (ii) their output layer parametrization is rigid because it depends on the dimensionality of the encoded text and labels, and (iii) they are outperformed on seen labels by classification baselines trained with cross-entropy loss (Frome et al., 2013; Socher et al., 2013). In this paper, we …

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