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        <title>Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches</title>
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        <namePart type="given">Yimeng</namePart>
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            <namePart type="given">Jonathan</namePart>
            <namePart type="family">May</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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            <namePart type="given">Xiaodan</namePart>
            <namePart type="family">Zhu</namePart>
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    <abstract>Suggestion mining task aims to extract tips, advice, and recommendations from unstructured text. The task includes many challenges, such as class imbalance, figurative expressions, context dependency, and long and complex sentences. This paper gives a detailed system description of our submission in SemEval 2019 Task 9 Subtask A. We transfer Self-Attention Network (SAN), a successful model in machine reading comprehension field, into this task. Our model concentrates on modeling long-term dependency which is indispensable to parse long and complex sentences. Besides, we also adopt techniques, such as contextualized embedding, back-translation, and auxiliary loss, to augment the system. Our model achieves a performance of F1=76.3, and rank 4th among 34 participating systems. Further ablation study shows that the techniques used in our system are beneficial to the performance.</abstract>
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    <identifier type="doi">10.18653/v1/S19-2222</identifier>
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%0 Conference Proceedings
%T Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches
%A Zhuang, Yimeng
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F zhuang-2019-yimmon
%X Suggestion mining task aims to extract tips, advice, and recommendations from unstructured text. The task includes many challenges, such as class imbalance, figurative expressions, context dependency, and long and complex sentences. This paper gives a detailed system description of our submission in SemEval 2019 Task 9 Subtask A. We transfer Self-Attention Network (SAN), a successful model in machine reading comprehension field, into this task. Our model concentrates on modeling long-term dependency which is indispensable to parse long and complex sentences. Besides, we also adopt techniques, such as contextualized embedding, back-translation, and auxiliary loss, to augment the system. Our model achieves a performance of F1=76.3, and rank 4th among 34 participating systems. Further ablation study shows that the techniques used in our system are beneficial to the performance.
%R 10.18653/v1/S19-2222
%U https://aclanthology.org/S19-2222
%U https://doi.org/10.18653/v1/S19-2222
%P 1267-1271

Markdown (Informal)

[Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches](https://aclanthology.org/S19-2222) (Zhuang, SemEval 2019)

• Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches (Zhuang, SemEval 2019)

ACL

• Yimeng Zhuang. 2019. Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1267–1271, Minneapolis, Minnesota, USA. Association for Computational Linguistics.