Semantic Textual Similarity

In this paper, we describe our system submit- ted for the semantic textual similarity (STS) task at SemEval 2012. We implemented two approaches to calculate the degree of simi- larity …...

Many problems in natural language process- ing can be viewed as variations of the task of measuring the semantic textual similarity be- tween short texts. However, many systems that address …...

In this paper we describe the three approaches we submitted to the Semantic Textual Similar- ity task of SemEval 2012. The first approach considers to calculate the semantic similar- ity …...

This paper describes the participation of the IRIT team to SemEval 2012 Task 6 (Seman- tic Textual Similarity). The method used con- sists of a n-gram based comparison method combined …...

We propose a semantic similarity learning method based on Random Indexing (RI) and ranking with boosting. Unlike classical RI, we use only those context vector features that are informative for …...

This paper presents the work of the Hong Kong Polytechnic University (PolyUCOMP) team which has participated in the Semantic Textual Similarity task of SemEval-2012. The PolyUCOMP system combines semantic vec- …...

In this paper we report the results obtained in the Semantic Textual Similarity (STS) task, with a system primarily developed for textual entailment. Our results are quite promising, getting a …...

In this paper, we describe the system architec- ture used in the Semantic Textual Similarity (STS) task 6 pilot challenge. The goal of this challenge is to accurately identify five …...

Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two texts. This paper presents the results of the STS pilot task in Semeval. The training data contained 2000 …...

We describe the systems submitted by SRI In- ternational and the University of the Basque Country for the Semantic Textual Similarity (STS) SemEval-2012 task. Our systems fo- cused on using …...

This paper briefly reports our submissions to the Semantic Textual Similarity (STS) task in the SemEval 2012 (Task 6). We first use knowledge-based methods to compute word semantic similarity as …...

The UOW submissions to the Semantic Tex- tual Similarity task at SemEval-2012 use a supervised machine learning algorithm along with features based on lexical, syntactic and semantic similarity metrics to …...

The Semantic Textual Similarity (STS) shared task (Agirre et al., 2012) computes the degree of semantic equivalence between two sen- tences.1 We show that a simple unsupervised latent semantics based …...

Semantic similarity is an essential com- ponent of many Natural Language Pro- cessing applications. However, prior meth- ods for computing semantic similarity of- ten operate at different levels, e.g., sin- …...

This paper describes the system that was sub- mitted in the *SEM 2013 Semantic Textual Similarity shared task. The task aims to find the similarity score between a pair of …...

This paper describes our submission for the *SEM shared task of Semantic Textual Sim- ilarity. We estimate the semantic similarity between two sentences using regression mod- els with features: 1) …...

We present a system submitted in the Semantic Textual Similarity (STS) task at the Second Joint Conference on Lexical and Computa- tional Semantics (*SEM 2013). Given two short text fragments, …...

This paper describes a system for automat- ically measuring the semantic similarity be- tween two texts, which was the aim of the 2013 Semantic Textual Similarity (STS) task (Agirre et …...

We present in this paper the systems we participated with in the Semantic Textual Similarity task at SEM 2013. The Semantic Textual Similarity Core task (STS) computes the degree of …...

This paper presents three methods to evaluate the Semantic Textual Similarity (STS). The first two methods do not require labeled train- ing data; instead, they automatically extract semantic knowledge in …...

This paper describes the system used by the LIPN team in the Semantic Textual Similarity task at *SEM 2013. It uses a support vector re- gression model, combining different text …...

The Semantic Textual Similarity (STS) task examines semantic similarity at a sentence- level. We explored three representations of semantics (implicit or explicit): named enti- ties, semantic vectors, and structured vectorial …...

The paper outlines the work carried out at NTNU as part of the *SEM’13 shared task on Semantic Textual Similarity, using an ap- proach which combines shallow textual, dis- tributional …...

In this year’s Semantic Textual Similarity evaluation, we explore the contribution of models that provide soft similarity scores across spans of multiple words, over the pre- vious year’s system. To …...

This paper describes the UNIBA participation in the Semantic Textual Similarity (STS) core task 2013. We exploited three different sys- tems for computing the similarity between two texts. A system …...

In this paper we present our systems for cal- culating the degree of semantic similarity be- tween two texts that we submitted to the Se- mantic Textual Similarity task at …...

This paper presents the UNITOR system that participated in the *SEM 2013 shared task on Semantic Textual Similarity (STS). The task is modeled as a Support Vector (SV) regression problem, …...

Probabilistic Soft Logic (PSL) is a re- cently developed framework for proba- bilistic logic. We use PSL to combine logical and distributional representations of natural-language meaning, where distri- butional information …...

ThisThis work is licensed under a Creative Commons Attribution 4.0 International Li- cence. Page numbers and proceedings footer are added by the organisers. Licence details: http://creativecommons.org/licenses/by/4.0/ paper describes the second …...

We describe the work carried out by the DCU team on the Semantic Textual Similarity task at SemEval-2015. We learn a regression model to predict a semantic similarity score between …...

This paper reports our submissions to seman- tic textual similarity task, i.e., task 2 in Se- mantic Evaluation 2015. We built our sys- tems using various traditional features, such as …...

We present a case study on the role of syn- tactic structures towards resolving the Se- mantic Textual Similarity (STS) task. Al- though various approaches have been pro- posed, the …...

We present a work to evaluate the hy- pothesis that automatic evaluation metrics developed for Machine Translation (MT) systems have significant impact on pre- dicting semantic similarity scores in Se- …...

This paper describes the system submitted by the University of Wolverhampton and the University of Malaga for SemEval-2015 Task 2: Semantic Textual Similarity. The system uses a Supported Vector Machine …...

In this shared task, we present evaluations on two related tasks Paraphrase Identification (PI) and Semantic Textual Similarity (SS) sys- tems for the Twitter data. Given a pair of sentences, …...

In semantic textual similarity (STS), systems rate the degree of semantic equivalence be- tween two text snippets. This year, the par- ticipants were challenged with new datasets in English and …...

In Semantic Textual Similarity, systems rate the degree of semantic equivalence on a graded scale from 0 to 5, with 5 being the most similar. For the English subtask, we …...

This paper presents an approach for estimat- ing the Semantic Textual Similarity of full English sentences as specified in Shared Task 2 of SemEval-2015. The semantic similarity of sentence pairs …...

Semantic Textual Similarity measures similar- ity between pair of texts, even though the sim- ilar context is projected using different words. This work attempted to incorporate the con- text space …...

A growing body of research has recently been conducted on semantic textual similarity using a variety of neural network models. While re- cent research focuses on word-based represen- tation for …...

AbstractWe present the dict_to_4lang tool for processing entries of three monolingual dictionaries of English and mapping definitions to concept graphs following the 4lang principles of semantic representation introduced by (Kornai, …...

In this paper we describe our system (DT- Sim) submitted at SemEval-2016 Task 1: Se- mantic Textual Similarity (STS Core). We developed Support Vector Regression model with various features including …...

Semantic Textual Similarity (STS) seeks to measure the degree of semantic equivalence between two snippets of text. Similarity is ex- pressed on an ordinal scale that spans from semantic equivalence …...

In this paper, a system for semantic tex- tual similarity, which participated in Task- 1 in SemEval 2016 (monolingual and cross- lingual sub-tasks) is described. The system contains a preprocessing …...

AbstractSemantic Textual Similarity (STS) is a foundational NLP task and can be used in a wide range of tasks. To determine the STS of two texts, hundreds of different STS …...

In this paper, we propose a deep neural net- work based natural language processing sys- tem for semantic textual similarity prediction. We leverage multi-layer bidirectional LSTM to learn sentence representation. …...

We present our UWB system for Seman- tic Textual Similarity (STS) task at SemEval 2016. Given two sentences, the system esti- mates the degree of their semantic similarity. We use …...

AbstractWe present our submitted systems for Semantic Textual Similarity (STS) Track 4 at SemEval-2017. Given a pair of Spanish-English sentences, each system must estimate their semantic similarity by a score …...

AbstractDetermining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, …...

AbstractWe describe our system (DT Team) submitted at SemEval-2017 Task 1, Semantic Textual Similarity (STS) challenge for English (Track 5). We developed three different models with various features including similarity …...

AbstractIn this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search …...

AbstractIn this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we …...

AbstractThis 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, …...

AbstractThis paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences …...

AbstractSemantic 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 …...

AbstractWe propose a novel discriminative ranking model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to …...

AbstractThis paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our …...

AbstractThis paper describes the model UdL we proposed to solve the semantic textual similarity task of SemEval 2017 workshop. The track we participated in was estimating the semantics relatedness of …...

Although the task of semantic textual similarity (STS) has gained in prominence in the last few years, annotated STS datasets for model training and evaluation, particularly those with fine-grained similarity …...

In this article, we describe FrNewsLink, a corpus allowing to address several applicative tasks that we make publicly available. It gathers several resources from TV Broadcast News (TVBN) shows and …...

AbstractDistributed representations of sentences have become ubiquitous in natural language processing tasks. In this paper, we consider a continual learning scenario for sentence representations: Given a sequence of corpora, we …...

AbstractA large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little …...

AbstractCalculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. All modern state of the art STS …...

AbstractBERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that …...

Multilingual language models have been a crucial breakthrough as they considerably re- duce the need of data for under-resourced languages. Nevertheless, the superiority of language-specific models has already been proven …...

AbstractAs high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention. In this paper, we propose a new framework with a two-branch Siamese Network which maximizes the …...

AbstractLearning high-quality sentence representations benefits a wide range of natural language processing tasks. Though BERT-based pre-trained language models achieve high performance on many downstream tasks, the native derived sentence representations …...

AbstractSemantic textual similarity (STS) systems estimate the degree of the meaning similarity between two sentences. Cross-lingual STS systems estimate the degree of the meaning similarity between two sentences, each in …...

AbstractContrastive learning has shown great potential in unsupervised sentence embedding tasks, e.g., SimCSE (CITATION).However, these existing solutions are heavily affected by superficial features like the length of sentences or syntactic …...

AbstractAbstract Meaning Representation (AMR) is a semantic representation for NLP/NLU. In this paper, we propose to use it for data augmentation in NLP. Our proposed data augmentation technique, called AMR-DA, …...

AbstractThe paper aims to identify cited text spans in the reference paper related to the given citance in the citing paper. We refer to it as cited text span retrieval …...

AbstractThere have been many successful applications of sentence embedding methods. However, it has not been well understood what properties are captured in the resulting sentence embeddings depending on the supervision …...

AbstractNatural Language Inference (NLI) and Semantic Textual Similarity (STS) are widely used benchmark tasks for compositional evaluation of pre-trained language models. Despite growing interest in linguistic universals, most NLI/STS studies …...

AbstractHow to learn highly compact yet effective sentence representation? Pre-trained language models have been effective in many NLP tasks. However, these models are often huge and produce large sentence embeddings. …...

Abstract“Learning sentence representation is a fundamental task in natural language processing and has been studied extensively. Recently, many works have obtained high-quality sentence representation based on contrastive learning from pre-trained …...

AbstractWe propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference between the original sentence and an edited sentence, …...

AbstractRunning large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing …...

AbstractRecent pre-trained language models (PLMs) achieved great success on many natural language processing tasks through learning linguistic features and contextualized sentence representation. Since attributes captured in stacked layers of PLMs …...

AbstractIn this paper, we present the participation of the EMBEDDIA team in the SemEval-2022 Task 8 (Multilingual News Article Similarity). We cover several techniques and propose different methods for finding …...

AbstractRecently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts …...

As Natural Language Processing is evolving rapidly, it is used to analyze domain spe- cific large text corpora. Applying Natural Language Processing in a domain with un- common vocabulary and …...

AbstractRecently, finetuning a pretrained language model to capture the similarity between sentence embeddings has shown the state-of-the-art performance on the semantic textual similarity (STS) task. However, the absence of an …...

AbstractVector representations of natural language are ubiquitous in search applications. Recently, various methods based on contrastive learning have been proposed to learn textual representations from unlabelled data; by maximizing alignment …...

AbstractText embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on …...