@inproceedings{henderson-etal-2017-mitre,
title = "{MITRE} at {S}em{E}val-2017 Task 1: Simple Semantic Similarity",
author = "Henderson, John and
Merkhofer, Elizabeth and
Strickhart, Laura and
Zarrella, Guido",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2027",
doi = "10.18653/v1/S17-2027",
pages = "185--190",
abstract = "This paper describes MITRE{'}s participation in the Semantic Textual Similarity task (SemEval-2017 Task 1), which evaluated machine learning approaches to the identification of similar meaning among text snippets in English, Arabic, Spanish, and Turkish. We detail the techniques we explored ranging from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Linear regression is used to tie the systems together into an ensemble submitted for evaluation. The resulting system is capable of matching human similarity ratings of image captions with correlations of 0.73 to 0.83 in monolingual settings and 0.68 to 0.78 in cross-lingual conditions, demonstrating the power of relatively simple approaches.",
}
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<abstract>This paper describes MITRE’s participation in the Semantic Textual Similarity task (SemEval-2017 Task 1), which evaluated machine learning approaches to the identification of similar meaning among text snippets in English, Arabic, Spanish, and Turkish. We detail the techniques we explored ranging from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Linear regression is used to tie the systems together into an ensemble submitted for evaluation. The resulting system is capable of matching human similarity ratings of image captions with correlations of 0.73 to 0.83 in monolingual settings and 0.68 to 0.78 in cross-lingual conditions, demonstrating the power of relatively simple approaches.</abstract>
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%0 Conference Proceedings
%T MITRE at SemEval-2017 Task 1: Simple Semantic Similarity
%A Henderson, John
%A Merkhofer, Elizabeth
%A Strickhart, Laura
%A Zarrella, Guido
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F henderson-etal-2017-mitre
%X This paper describes MITRE’s participation in the Semantic Textual Similarity task (SemEval-2017 Task 1), which evaluated machine learning approaches to the identification of similar meaning among text snippets in English, Arabic, Spanish, and Turkish. We detail the techniques we explored ranging from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Linear regression is used to tie the systems together into an ensemble submitted for evaluation. The resulting system is capable of matching human similarity ratings of image captions with correlations of 0.73 to 0.83 in monolingual settings and 0.68 to 0.78 in cross-lingual conditions, demonstrating the power of relatively simple approaches.
%R 10.18653/v1/S17-2027
%U https://aclanthology.org/S17-2027
%U https://doi.org/10.18653/v1/S17-2027
%P 185-190
Markdown (Informal)
[MITRE at SemEval-2017 Task 1: Simple Semantic Similarity](https://aclanthology.org/S17-2027) (Henderson et al., SemEval 2017)
ACL
- John Henderson, Elizabeth Merkhofer, Laura Strickhart, and Guido Zarrella. 2017. MITRE at SemEval-2017 Task 1: Simple Semantic Similarity. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 185–190, Vancouver, Canada. Association for Computational Linguistics.