Makoto Yamada


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Computationally Efficient Wasserstein Loss for Structured Labels
Ayato Toyokuni | Sho Yokoi | Hisashi Kashima | Makoto Yamada
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop

The problem of estimating the probability distribution of labels has been widely studied as a label distribution learning (LDL) problem, whose applications include age estimation, emotion analysis, and semantic segmentation. We propose a tree-Wasserstein distance regularized LDL algorithm, focusing on hierarchical text classification tasks. We propose predicting the entire label hierarchy using neural networks, where the similarity between predicted and true labels is measured using the tree-Wasserstein distance. Through experiments using synthetic and real-world datasets, we demonstrate that the proposed method successfully considers the structure of labels during training, and it compares favorably with the Sinkhorn algorithm in terms of computation time and memory usage.


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Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel
Yao-Hung Hubert Tsai | Shaojie Bai | Makoto Yamada | Louis-Philippe Morency | Ruslan Salakhutdinov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction. At the core of the Transformer is the attention mechanism, which concurrently processes all inputs in the streams. In this paper, we present a new formulation of attention via the lens of the kernel. To be more precise, we realize that the attention can be seen as applying kernel smoother over the inputs with the kernel scores being the similarities between inputs. This new formulation gives us a better way to understand individual components of the Transformer’s attention, such as the better way to integrate the positional embedding. Another important advantage of our kernel-based formulation is that it paves the way to a larger space of composing Transformer’s attention. As an example, we propose a new variant of Transformer’s attention which models the input as a product of symmetric kernels. This approach achieves competitive performance to the current state of the art model with less computation. In our experiments, we empirically study different kernel construction strategies on two widely used tasks: neural machine translation and sequence prediction.


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Learning Unsupervised Word Translations Without Adversaries
Tanmoy Mukherjee | Makoto Yamada | Timothy Hospedales
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks. Recent research has shown that word translation can be achieved in an unsupervised manner, without parallel seed dictionaries or aligned corpora. However, state of the art methods unsupervised bilingual dictionary induction are based on generative adversarial models, and as such suffer from their well known problems of instability and hyper-parameter sensitivity. We present a statistical dependency-based approach to bilingual dictionary induction that is unsupervised – no seed dictionary or parallel corpora required; and introduces no adversary – therefore being much easier to train. Our method performs comparably to adversarial alternatives and outperforms prior non-adversarial methods.


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Which Tumblr Post Should I Read Next?
Zornitsa Kozareva | Makoto Yamada
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)