UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model
            for Semantic Textual Similarity

Barrow, Joe; Peskov, Denis

doi:10.18653/v1/s17-2026

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…The architecture is abstractly similar to ECNU's deep learning models. UMDeep (Barrow and Peskov, 2017) took a similar approach using LSTMs rather than CNNs for the sentence embeddings.…”

Section: Methodsmentioning

confidence: 99%

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Cer¹,

Diab²,

Agirre³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

1,031

775

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Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012)(2013)(2014)(2015)(2016)(2017).

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“…The architecture is abstractly similar to ECNU's deep learning models. UMDeep (Barrow and Peskov, 2017) took a similar approach using LSTMs rather than CNNs for the sentence embeddings.…”

Section: Methodsmentioning

confidence: 99%

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Cer¹,

Diab²,

Agirre³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

1,031

775

View full text Add to dashboard Cite

show abstract

“…Tian and Lan [27] employed the sentence embedding and word-embedding to measure the semantic textual similarity. Some other works also used word-embedding for semantic similarity measurement such as [28], [29]. The mentioned related works did not consider the grammatical structure of the sentences to estimate the semantics similarity, but the grammatical structure holds different properties of the sentences that might be useful to compute the similarity.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Grammatical Roles for Measuring Semantic Similarity Between Texts

et al. 2021

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Semantic similarity between texts can be defined based on their meaning. Assessing the textual similarity is a prerequisite in almost all applications in the field of language processing and information retrieval. However, the diversity in the sentence structure makes it formidable to estimate the similarity. Some sentences pairs are lexicographically similar but semantically dissimilar. That is why the trivial lexical overlapping is not enough for measuring the similarity. To attain the semanticity of sentences, the context of the words and the structure of the sentence should be considered. In this paper, we propose a new method for capturing the semantic similarity between sentences based on their grammatical roles through word semantics. First, the sentences are divided grammatically into different parts where each part is considered as a grammatical role. Then multiple new measures are introduced to estimate the role-based similarity exploiting word semantics considering the sentence structure. The proposed similarity measures focus on inter-role and intra-role similarity between the sentence-pair. The word-level semantic information is extracted from a pre-trained word-embedding model. The performance of the proposed method was verified by conducting a wide range of experiments on the SemEval STS dataset. The experimental results indicated the effectiveness of the proposed method in terms of different standard evaluation metrics and outperformed some known related works.

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Exploiting Siamese Neural Networks on Short Text Similarity Tasks for Multiple Domains and Languages

Souza

Oliveira

Gumiel

et al. 2020

Lecture Notes in Computer Science

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UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity

Cited by 5 publications

References 6 publications

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Leveraging Grammatical Roles for Measuring Semantic Similarity Between Texts

Exploiting Siamese Neural Networks on Short Text Similarity Tasks for Multiple Domains and Languages

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