Carlos Escolano


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Multilingual, Multi-scale and Multi-layer Visualization of Intermediate Representations
Carlos Escolano | Marta R. Costa-jussà | Elora Lacroux | Pere-Pau Vázquez
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

The main alternatives nowadays to deal with sequences are Recurrent Neural Networks (RNN) architectures and the Transformer. In this context, Both RNN’s and Transformer have been used as an encoder-decoder architecture with multiple layers in each module. Far beyond this, these architectures are the basis for the contextual word embeddings which are revolutionizing most natural language downstream applications. However, intermediate representations in either the RNN or Transformer architectures can be difficult to interpret. To make these layer representations more accessible and meaningful, we introduce a web-based tool that visualizes them both at the sentence and token level. We present three use cases. The first analyses gender issues in contextual word embeddings. The second and third are showing multilingual intermediate representations for sentences and tokens and the evolution of these intermediate representations along with the multiple layers of the decoder and in the context of multilingual machine translation.

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From Bilingual to Multilingual Neural Machine Translation by Incremental Training
Carlos Escolano | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Multilingual Neural Machine Translation approaches are based on the use of task specific models and the addition of one more language can only be done by retraining the whole system. In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation. This work in progress shows close results to state-of-the-art in the WMT task.

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The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT
Noe Casas | José A. R. Fonollosa | Carlos Escolano | Christine Basta | Marta R. Costa-jussà
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

In this article, we describe the TALP-UPC research group participation in the WMT19 news translation shared task for Kazakh-English. Given the low amount of parallel training data, we resort to using Russian as pivot language, training subword-based statistical translation systems for Russian-Kazakh and Russian-English that were then used to create two synthetic pseudo-parallel corpora for Kazakh-English and English-Kazakh respectively. Finally, a self-attention model based on the decoder part of the Transformer architecture was trained on the two pseudo-parallel corpora.


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The TALP-UPC Machine Translation Systems for WMT18 News Shared Translation Task
Noe Casas | Carlos Escolano | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

In this article we describe the TALP-UPC research group participation in the WMT18 news shared translation task for Finnish-English and Estonian-English within the multi-lingual subtrack. All of our primary submissions implement an attention-based Neural Machine Translation architecture. Given that Finnish and Estonian belong to the same language family and are similar, we use as training data the combination of the datasets of both language pairs to paliate the data scarceness of each individual pair. We also report the translation quality of systems trained on individual language pair data to serve as baseline and comparison reference.


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Byte-based Neural Machine Translation
Marta R. Costa-jussà | Carlos Escolano | José A. R. Fonollosa
Proceedings of the First Workshop on Subword and Character Level Models in NLP

This paper presents experiments comparing character-based and byte-based neural machine translation systems. The main motivation of the byte-based neural machine translation system is to build multi-lingual neural machine translation systems that can share the same vocabulary. We compare the performance of both systems in several language pairs and we see that the performance in test is similar for most language pairs while the training time is slightly reduced in the case of byte-based neural machine translation.

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The TALP-UPC Neural Machine Translation System for German/Finnish-English Using the Inverse Direction Model in Rescoring
Carlos Escolano | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the Second Conference on Machine Translation


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The TALPUPC Spanish–English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System
Marta R. Costa-jussà | Cristina España-Bonet | Pranava Madhyastha | Carlos Escolano | José A. R. Fonollosa
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers