Shigehiko Schamoni


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Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation
Tsz Kin Lam | Shigehiko Schamoni | Stefan Riezler
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations into a target language. Such data are notoriously scarce, making synthetic data augmentation by back-translation or knowledge distillation a necessary ingredient of end-to-end training. In this paper, we present a novel approach to data augmentation that leverages audio alignments, linguistic properties, and translation. First, we augment a transcription by sampling from a suffix memory that stores text and audio data. Second, we translate the augmented transcript. Finally, we recombine concatenated audio segments and the generated translation. Our method delivers consistent improvements of up to 0.9 and 1.1 BLEU points on top of augmentation with knowledge distillation on five language pairs on CoVoST 2 and on two language pairs on Europarl-ST, respectively.


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Embedding Meta-Textual Information for Improved Learning to Rank
Toshitaka Kuwa | Shigehiko Schamoni | Stefan Riezler
Proceedings of the 28th International Conference on Computational Linguistics

Neural approaches to learning term embeddings have led to improved computation of similarity and ranking in information retrieval (IR). So far neural representation learning has not been extended to meta-textual information that is readily available for many IR tasks, for example, patent classes in prior-art retrieval, topical information in Wikipedia articles, or product categories in e-commerce data. We present a framework that learns embeddings for meta-textual categories, and optimizes a pairwise ranking objective for improved matching based on combined embeddings of textual and meta-textual information. We show considerable gains in an experimental evaluation on cross-lingual retrieval in the Wikipedia domain for three language pairs, and in the Patent domain for one language pair. Our results emphasize that the mode of combining different types of information is crucial for model improvement.


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Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation
Tsz Kin Lam | Shigehiko Schamoni | Stefan Riezler
Proceedings of Machine Translation Summit XVII: Research Track


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A Dataset and Reranking Method for Multimodal MT of User-Generated Image Captions
Shigehiko Schamoni | Julian Hitschler | Stefan Riezler
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Cross-Lingual Learning-to-Rank with Shared Representations
Shota Sasaki | Shuo Sun | Shigehiko Schamoni | Kevin Duh | Kentaro Inui
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user’s query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. Further, we present a simple yet effective neural learning-to-rank model that shares representations across languages and reduces the data requirement. This model can exploit training data in, for example, Japanese-English CLIR to improve the results of Swahili-English CLIR.


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Multimodal Pivots for Image Caption Translation
Julian Hitschler | Shigehiko Schamoni | Stefan Riezler
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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QUality Estimation from ScraTCH (QUETCH): Deep Learning for Word-level Translation Quality Estimation
Julia Kreutzer | Shigehiko Schamoni | Stefan Riezler
Proceedings of the Tenth Workshop on Statistical Machine Translation


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Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval
Shigehiko Schamoni | Felix Hieber | Artem Sokolov | Stefan Riezler
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)