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        <title>Neural Paraphrase Generation with Stacked Residual LSTM Networks</title>
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        <namePart type="given">Aaditya</namePart>
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    <name type="personal">
        <namePart type="given">Vivek</namePart>
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    <abstract>In this paper, we propose a novel neural approach for paraphrase generation. Conventional paraphrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation. Our primary contribution is a stacked residual LSTM network, where we add residual connections between LSTM layers. This allows for efficient training of deep LSTMs. We evaluate our model and other state-of-the-art deep learning models on three different datasets: PPDB, WikiAnswers, and MSCOCO. Evaluation results demonstrate that our model outperforms sequence to sequence, attention-based, and bi-directional LSTM models on BLEU, METEOR, TER, and an embedding-based sentence similarity metric.</abstract>
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%0 Conference Proceedings
%T Neural Paraphrase Generation with Stacked Residual LSTM Networks
%A Prakash, Aaditya
%A Hasan, Sadid A.
%A Lee, Kathy
%A Datla, Vivek
%A Qadir, Ashequl
%A Liu, Joey
%A Farri, Oladimeji
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 dec
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F prakash-etal-2016-neural
%X In this paper, we propose a novel neural approach for paraphrase generation. Conventional paraphrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation. Our primary contribution is a stacked residual LSTM network, where we add residual connections between LSTM layers. This allows for efficient training of deep LSTMs. We evaluate our model and other state-of-the-art deep learning models on three different datasets: PPDB, WikiAnswers, and MSCOCO. Evaluation results demonstrate that our model outperforms sequence to sequence, attention-based, and bi-directional LSTM models on BLEU, METEOR, TER, and an embedding-based sentence similarity metric.
%U https://aclanthology.org/C16-1275
%P 2923-2934

Markdown (Informal)

[Neural Paraphrase Generation with Stacked Residual LSTM Networks](https://aclanthology.org/C16-1275) (Prakash et al., COLING 2016)

• Neural Paraphrase Generation with Stacked Residual LSTM Networks (Prakash et al., COLING 2016)

ACL

• Aaditya Prakash, Sadid A. Hasan, Kathy Lee, Vivek Datla, Ashequl Qadir, Joey Liu, and Oladimeji Farri. 2016. Neural Paraphrase Generation with Stacked Residual LSTM Networks. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2923–2934, Osaka, Japan. The COLING 2016 Organizing Committee.