UWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks

Konopík, Miloslav; Pražák, Ondřej; Steinberger, David; Brychcín, Tomáš

doi:10.18653/v1/s16-1124

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…From the results we can see that labeling the type was the most challenging. Regarding the overall test results for type and score (+TS) across datasets, UWB (Konopík et al, 2016) and DTSim (Banjade et al, 2016) obtained the best results for the gold chunks scenario, and DTSim and FBK-HLT-NLP (Magnolini et al, 2016) for the system chunks scenario. In addition, DTSim obtained the best overall results even though they have not good results for the Answer-Students dataset.…”

Section: Resultsmentioning

confidence: 99%

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SemEval-2016 Task 2: Interpretable Semantic Textual Similarity

Agirre¹,

González-Agirre²,

López-Gazpio³

et al. 2016

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

103

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The final goal of Interpretable Semantic Textual Similarity (iSTS) is to build systems that explain which are the differences and commonalities between two sentences. The task adds an explanatory level on top of STS, formalized as an alignment between the chunks in the two input sentences, indicating the relation and similarity score of each alignment. The task provides train and test data on three datasets: news headlines, image captions and student answers. It attracted nine teams, totaling 20 runs. All datasets and the annotation guideline are freely available 1

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Section: Resultsmentioning

confidence: 99%

“…• UWB (Konopík et al, 2016): UWB used three separate supervised classifiers to perform alignment, scoring and typing. They defined a similarity function based on a distribution similarity paradigm: vector composition, lexical semantic vectors and iDF weighting.…”

Section: Systems Tools and Resourcesmentioning

confidence: 99%

SemEval-2016 Task 2: Interpretable Semantic Textual Similarity

Agirre¹,

González-Agirre²,

López-Gazpio³

et al. 2016

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

103

View full text Add to dashboard Cite

show abstract

“…One related task is interpretable STS, which aims to predict chunk alignment between two sentences . For this task, a variety of supervised approaches were proposed based on neural networks (Konopík et al, 2016;, linear programming (Tekumalla and Jat, 2016), and pretrained models (Maji et al, 2020). However, these methods cannot predict the similarity between sentences because they focus on finding chunk alignment only.…”

Section: Semantic Textual Similaritymentioning

confidence: 99%

Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning

Lee¹,

Lee²,

Jang³

et al. 2022

Preprint

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Recently, finetuning a pretrained language model to capture the similarity between sentence embeddings has shown the state-of-theart performance on the semantic textual similarity (STS) task. However, the absence of an interpretation method for the sentence similarity makes it difficult to explain the model output. In this work, we explicitly describe the sentence distance as the weighted sum of contextualized token distances on the basis of a transportation problem, and then present the optimal transport-based distance measure, named RCMD; it identifies and leverages semantically-aligned token pairs. In the end, we propose CLRCMD, a contrastive learning framework that optimizes RCMD of sentence pairs, which enhances the quality of sentence similarity and their interpretation. Extensive experiments demonstrate that our learning framework outperforms other baselines on both STS and interpretable-STS benchmarks, indicating that it computes effective sentence similarity and also provides interpretation consistent with human judgement.

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“…Konopik et al [40] Introduced a system for estimation of semantic textual similarity in SemEval 2016. The core of this system consisted of exploiting distributional semantics to compare the similarity of sentence chunks.…”

Section: Literature Reviewmentioning

confidence: 99%

Textual Similarity Measurement Approaches: A Survey (1)

Abo-Elghit

Al-Zoghby

Hamza

2020

The Egyptian Journal of Language Engineering

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Survey research is appropriate and necessary to address certain research question types. This paper aims to provide a general overview of the textual similarity in the literature. Measuring textual similarity tends to have an increasingly important turn in related topics like text classification, recovery of specific information from data, clustering, topic retrieval, subject tracking, question answering, essay grading, summarization, and the nowadays trending Conversational Agents (CA), which is a program deals with humans through natural language conversation. Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities. In particular, we concern with textual similarity in Arabic. In the Arabic Language, applying Natural language Processing (NLP) tasks are very challenging indeed as it has many characteristics, which are considered as confrontations. However, many approaches for measuring textual similarity have been presented for Arabic text reviewed and compared in this paper.

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UWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks

Cited by 12 publications

References 16 publications

SemEval-2016 Task 2: Interpretable Semantic Textual Similarity

SemEval-2016 Task 2: Interpretable Semantic Textual Similarity

Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning

Textual Similarity Measurement Approaches: A Survey (1)

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