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NUIG-UNLP At SemEval-2016 Task 1: Soft Alignment And Deep Learning For Semantic Textual Similarity.pdf

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Preview of NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity

🔗 Source: aclanthology.org

📊 Size: 475 KB

👤 Author: John Philip McCrae ; Kartik Asooja ; Nitish Aggarwal ; Paul Buitelaar

⬇️ Downloads: 54

Summary

They introduce soft alignment, a method that produces a score indicating the likelihood of words in one sentence aligning with words in another, instead of creating hard word correspondences.

Key Contributions:

Multi-Feature System: Combines various features based on simple metrics (like LCS, n-gram overlap) and novel deep learning techniques for robust semantic similarity calculation.
Soft Alignment: Leverages pre-trained neural word embeddings (WordSim) to create soft alignment matrices reflecting the semantic relatedness between words across sentences.
* Deep Learning Model: Uses a BiLSTM network to capture word context within sentences, complementing the soft alignment information.

Evaluation:

The system was evaluated on five datasets at SemEval 2016 STS Task 1, achieving above-median scores in four of them. The authors also report Pearson's Correlation for various configurations during development (see Table 1), demonstrating the effectiveness of their multi-feature approach.

Comparison:

They compare their system to Sultan et al.'s method (with and without Jacana aligner) and demonstrate significant improvements, especially when incorporating soft alignment and deep learning components.

Overall:

The paper presents a comprehensive exploration of different techniques for semantic textual similarity, highlighting the benefits of combining simple metrics with novel deep learning approaches like soft alignment and BiLSTM networks.

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Description

The paper introduces a multi-feature system for semantic text similarity, utilizing soft alignment and Explicit Semantic Analysis techniques, achieving top performance in 4 out of 5 datasets at SemEval-2016 Task 1.

Technical Information

File Format: PDF
File Size: 475 KB
Pages: 6
Language: EN
Author: John Philip McCrae ; Kartik Asooja ; Nitish Aggarwal ; Paul Buitelaar
Total Downloads: 54
Last Updated: 4 hours ago

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NUIG-UNLP At SemEval-2016 Task 1: Soft Alignment And Deep Learning For Semantic Textual Similarity.pdf

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