Gerasimos Lampouras


2020

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Proceedings of the Fourth Workshop on Structured Prediction for NLP
Priyanka Agrawal | Zornitsa Kozareva | Julia Kreutzer | Gerasimos Lampouras | André Martins | Sujith Ravi | Andreas Vlachos
Proceedings of the Fourth Workshop on Structured Prediction for NLP

2019

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Proceedings of the Third Workshop on Structured Prediction for NLP
Andre Martins | Andreas Vlachos | Zornitsa Kozareva | Sujith Ravi | Gerasimos Lampouras | Vlad Niculae | Julia Kreutzer
Proceedings of the Third Workshop on Structured Prediction for NLP

2017

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Imitation learning for structured prediction in natural language processing
Andreas Vlachos | Gerasimos Lampouras | Sebastian Riedel
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts

Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e.g. autonomous flight from pilot demonstrations. Recently, algorithms for structured prediction were proposed under this paradigm and have been applied successfully to a number of tasks including syntactic dependency parsing, information extraction, coreference resolution, dynamic feature selection, semantic parsing and natural language generation. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Our aim in this tutorial is to have a unified presentation of the various imitation algorithms for structure prediction, and show how they can be applied to a variety of NLP tasks.All material associated with the tutorial will be made available through https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017/.

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Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.
Gerasimos Lampouras | Andreas Vlachos
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the submission by the University of Sheffield to the SemEval 2017 Abstract Meaning Representation Parsing and Generation task (SemEval 2017 Task 9, Subtask 2). We cast language generation from AMR as a sequence of actions (e.g., insert/remove/rename edges and nodes) that progressively transform the AMR graph into a dependency parse tree. This transition-based approach relies on the fact that an AMR graph can be considered structurally similar to a dependency tree, with a focus on content rather than function words. An added benefit to this approach is the greater amount of data we can take advantage of to train the parse-to-text linearizer. Our submitted run on the test data achieved a BLEU score of 3.32 and a Trueskill score of -22.04 on automatic and human evaluation respectively.

2016

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Imitation learning for language generation from unaligned data
Gerasimos Lampouras | Andreas Vlachos
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Natural language generation (NLG) is the task of generating natural language from a meaning representation. Current rule-based approaches require domain-specific and manually constructed linguistic resources, while most machine-learning based approaches rely on aligned training data and/or phrase templates. The latter are needed to restrict the search space for the structured prediction task defined by the unaligned datasets. In this work we propose the use of imitation learning for structured prediction which learns an incremental model that handles the large search space by avoiding explicit enumeration of the outputs. We focus on the Locally Optimal Learning to Search framework which allows us to train against non-decomposable loss functions such as the BLEU or ROUGE scores while not assuming gold standard alignments. We evaluate our approach on three datasets using both automatic measures and human judgements and achieve results comparable to the state-of-the-art approaches developed for each of them.

2013

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Using Integer Linear Programming for Content Selection, Lexicalization, and Aggregation to Produce Compact Texts from OWL Ontologies
Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the 14th European Workshop on Natural Language Generation

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Using Integer Linear Programming in Concept-to-Text Generation to Produce More Compact Texts
Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Extractive Multi-Document Summarization with Integer Linear Programming and Support Vector Regression
Dimitrios Galanis | Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of COLING 2012

2009

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Finding Short Definitions of Terms on Web Pages
Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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An Open-Source Natural Language Generator for OWL Ontologies and its Use in Protege and Second Life
Dimitrios Galanis | George Karakatsiotis | Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the Demonstrations Session at EACL 2009

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Adaptive Natural Language Interaction
Stasinos Konstantopoulos | Athanasios Tegos | Dimitrios Bilidas | Ion Androutsopoulos | Gerasimos Lampouras | Colin Matheson | Olivier Deroo | Prodromos Malakasiotis
Proceedings of the Demonstrations Session at EACL 2009