Li Gong


2019

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Enhanced Transformer Model for Data-to-Text Generation
Li Gong | Josep Crego | Jean Senellart
Proceedings of the 3rd Workshop on Neural Generation and Translation

Neural models have recently shown significant progress on data-to-text generation tasks in which descriptive texts are generated conditioned on database records. In this work, we present a new Transformer-based data-to-text generation model which learns content selection and summary generation in an end-to-end fashion. We introduce two extensions to the baseline transformer model: First, we modify the latent representation of the input, which helps to significantly improve the content correctness of the output summary; Second, we include an additional learning objective that accounts for content selection modelling. In addition, we propose two data augmentation methods that succeed to further improve performance of the resulting generation models. Evaluation experiments show that our final model outperforms current state-of-the-art systems as measured by different metrics: BLEU, content selection precision and content ordering. We made publicly available the transformer extension presented in this paper.

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SYSTRAN @ WNGT 2019: DGT Task
Li Gong | Josep Crego | Jean Senellart
Proceedings of the 3rd Workshop on Neural Generation and Translation

This paper describes SYSTRAN participation to the Document-level Generation and Trans- lation (DGT) Shared Task of the 3rd Workshop on Neural Generation and Translation (WNGT 2019). We participate for the first time using a Transformer network enhanced with modified input embeddings and optimising an additional objective function that considers content selection. The network takes in structured data of basketball games and outputs a summary of the game in natural language.

2018

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Tencent Neural Machine Translation Systems for WMT18
Mingxuan Wang | Li Gong | Wenhuan Zhu | Jun Xie | Chao Bian
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We participated in the WMT 2018 shared news translation task on English↔Chinese language pair. Our systems are based on attentional sequence-to-sequence models with some form of recursion and self-attention. Some data augmentation methods are also introduced to improve the translation performance. The best translation result is obtained with ensemble and reranking techniques. Our Chinese→English system achieved the highest cased BLEU score among all 16 submitted systems, and our English→Chinese system ranked the third out of 18 submitted systems.

2015

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LIMSI: Translations as Source of Indirect Supervision for Multilingual All-Words Sense Disambiguation and Entity Linking
Marianna Apidianaki | Li Gong
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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LIMSI @ WMT’14 Medical Translation Task
Nicolas Pécheux | Li Gong | Quoc Khanh Do | Benjamin Marie | Yulia Ivanishcheva | Alexander Allauzen | Thomas Lavergne | Jan Niehues | Aurélien Max | François Yvon
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Towards a More Efficient Development of Statistical Machine Translation Systems (Vers un développement plus efficace des systèmes de traduction statistique : un peu de vert dans un monde de BLEU) [in French]
Li Gong | Aurélien Max | François Yvon
Proceedings of TALN 2014 (Volume 2: Short Papers)

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(Much) Faster Construction of SMT Phrase Tables from Large-scale Parallel Corpora (Construction (très) rapide de tables de traduction à partir de grands bi-textes) [in French]
Li Gong | Aurélien Max | François Yvon
Proceedings of TALN 2014 (Volume 3: System Demonstrations)

2012

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LIMSI @ WMT12
Hai-Son Le | Thomas Lavergne | Alexandre Allauzen | Marianna Apidianaki | Li Gong | Aurélien Max | Artem Sokolov | Guillaume Wisniewski | François Yvon
Proceedings of the Seventh Workshop on Statistical Machine Translation