Stephan Gouws


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Tensor2Tensor for Neural Machine Translation
Ashish Vaswani | Samy Bengio | Eugene Brevdo | Francois Chollet | Aidan Gomez | Stephan Gouws | Llion Jones | Łukasz Kaiser | Nal Kalchbrenner | Niki Parmar | Ryan Sepassi | Noam Shazeer | Jakob Uszkoreit
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)


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Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models
Yuanlong Shao | Stephan Gouws | Denny Britz | Anna Goldie | Brian Strope | Ray Kurzweil
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response. Unlike translation, conversation responding is inherently creative. The generation of long, informative, coherent, and diverse responses remains a hard task. In this work, we focus on the single turn setting. We add self-attention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets. We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process. We trained on a combined data set of over 2.3B conversation messages mined from the web. In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequence-to-sequence models with explicit length-promotion. A back-off strategy produces better responses overall, in the full spectrum of lengths.


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Simple task-specific bilingual word embeddings
Stephan Gouws | Anders Søgaard
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Deep Unsupervised Feature Learning for Natural Language Processing
Stephan Gouws
Proceedings of the NAACL HLT 2012 Student Research Workshop


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Contextual Bearing on Linguistic Variation in Social Media
Stephan Gouws | Donald Metzler | Congxing Cai | Eduard Hovy
Proceedings of the Workshop on Language in Social Media (LSM 2011)

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Unsupervised Mining of Lexical Variants from Noisy Text
Stephan Gouws | Dirk Hovy | Donald Metzler
Proceedings of the First workshop on Unsupervised Learning in NLP


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Measuring Conceptual Similarity by Spreading Activation over Wikipedia’s Hyperlink Structure
Stephan Gouws | G-J van Rooyen | Herman A. Engelbrecht
Proceedings of the 2nd Workshop on The People’s Web Meets NLP: Collaboratively Constructed Semantic Resources