Abstract
We present in this paper our team LCTMALTA's submission to the RepEval 2017 Shared Task on natural language inference. Our system is a simple system based on a standard BiLSTM architecture, using as input GloVe word embeddings augmented with further linguistic information. We use max pooling on the BiLSTM outputs to obtain embeddings for sentences. On both the matched and the mismatched test sets, our system clearly beats the shared task's BiLSTM baseline model.
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CITATION STYLE
Vu, H. T., Pham, T. H., Bai, X., Tanti, M., Plas, L. V. D., & Gatt, A. (2017). LCT-Malta’s Submission to RepEval 2017 Shared Task. In RepEval 2017 - 2nd Workshop on Evaluating Vector-Space Representations for NLP, Proceedings of the Workshop (pp. 56–60). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-5311
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