SemEval-2010 Task 12: Parser Evaluation using Textual Entailments

Deniz Yuret, Aydın Han, Zehra Turgut. Proceedings of the 5th International Workshop on Semantic Evaluation. (SemEval-2010) pp. 51--56. July, 2010. Uppsala, Sweden. (PDF, Presentation, Task website, Proceedings, Journal submission).



Abstract: Parser Evaluation using Textual Entailments (PETE) is a shared task in the SemEval-2010 Evaluation Exercises on Semantic Evaluation. The task involves recognizing textual entailments based on syntactic information alone. PETE introduces a new parser evaluation scheme that is formalism independent, less prone to annotation error, and focused on semantically relevant distinctions.

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L1 Regularized Regression for Reranking and System Combination in Machine Translation

Ergun Bicici, Deniz Yuret. Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR. pp. 282--289. July 2010. Uppsala, Sweden. (PDF, Slide, Poster)

Abstract: We use L1 regularized transductive regression to learn mappings between source and target features of the training sets derived for each test sentence and use these mappings to rerank translation outputs. We compare the effectiveness of L1 regularization techniques for regression to learn mappings between features given in a sparse feature matrix. The results show the effectiveness of using L1 regularization versus L2 used in ridge regression. We show that regression mapping is effective in reranking translation outputs and in selecting the best system combinations with encouraging results on different language pairs.

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