Elena Simperl


2018

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T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples
Hady Elsahar | Pavlos Vougiouklis | Arslen Remaci | Christophe Gravier | Jonathon Hare | Frederique Laforest | Elena Simperl
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata
Lucie-Aimée Kaffee | Hady Elsahar | Pavlos Vougiouklis | Christophe Gravier | Frédérique Laforest | Jonathon Hare | Elena Simperl
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

While Wikipedia exists in 287 languages, its content is unevenly distributed among them. In this work, we investigate the generation of open domain Wikipedia summaries in underserved languages using structured data from Wikidata. To this end, we propose a neural network architecture equipped with copy actions that learns to generate single-sentence and comprehensible textual summaries from Wikidata triples. We demonstrate the effectiveness of the proposed approach by evaluating it against a set of baselines on two languages of different natures: Arabic, a morphological rich language with a larger vocabulary than English, and Esperanto, a constructed language known for its easy acquisition.

2016

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Aligning Texts and Knowledge Bases with Semantic Sentence Simplification
Yassine Mrabet | Pavlos Vougiouklis | Halil Kilicoglu | Claire Gardent | Dina Demner-Fushman | Jonathon Hare | Elena Simperl
Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016)

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A Neural Network Approach for Knowledge-Driven Response Generation
Pavlos Vougiouklis | Jonathon Hare | Elena Simperl
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We present a novel response generation system. The system assumes the hypothesis that participants in a conversation base their response not only on previous dialog utterances but also on their background knowledge. Our model is based on a Recurrent Neural Network (RNN) that is trained over concatenated sequences of comments, a Convolution Neural Network that is trained over Wikipedia sentences and a formulation that couples the two trained embeddings in a multimodal space. We create a dataset of aligned Wikipedia sentences and sequences of Reddit utterances, which we we use to train our model. Given a sequence of past utterances and a set of sentences that represent the background knowledge, our end-to-end learnable model is able to generate context-sensitive and knowledge-driven responses by leveraging the alignment of two different data sources. Our approach achieves up to 55% improvement in perplexity compared to purely sequential models based on RNNs that are trained only on sequences of utterances.