Search Results for author: Gongbo Tang

Found 9 papers, 4 papers with code

Revisiting Negation in Neural Machine Translation

1 code implementation26 Jul 2021 Gongbo Tang, Philipp Rönchen, Rico Sennrich, Joakim Nivre

In this paper, we evaluate the translation of negation both automatically and manually, in English--German (EN--DE) and English--Chinese (EN--ZH).

Machine Translation Translation

Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English

no code implementations COLING 2020 Gongbo Tang, Rico Sennrich, Joakim Nivre

The attention distribution pattern shows that separators attract a lot of attention and we explore a sparse word-level attention to enforce character hidden states to capture the full word-level information.

Machine Translation Translation

Encoders Help You Disambiguate Word Senses in Neural Machine Translation

no code implementations IJCNLP 2019 Gongbo Tang, Rico Sennrich, Joakim Nivre

We find that encoder hidden states outperform word embeddings significantly which indicates that encoders adequately encode relevant information for disambiguation into hidden states.

Machine Translation Translation +1

Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models

no code implementations RANLP 2019 Gongbo Tang, Rico Sennrich, Joakim Nivre

In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models.

Machine Translation Translation +1

An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation

no code implementations WS 2018 Gongbo Tang, Rico Sennrich, Joakim Nivre

Recent work has shown that the encoder-decoder attention mechanisms in neural machine translation (NMT) are different from the word alignment in statistical machine translation.

Machine Translation Translation +2

An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization

1 code implementation COLING 2018 Gongbo Tang, Fabienne Cap, Eva Pettersson, Joakim Nivre

In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish.

Machine Translation Translation

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