Papers STS Benchmark
“STS Benchmark” 태그가 달린 논문 16편 · 필터 해제
GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings
Training-free embedding methods directly leverage pretrained large language models (LLMs) to embed text, bypassing the costly and complex procedure of contrastive learning. Previous training-free embedding methods have m…
Contrastive LearningMTEB BenchmarkRerankingSentence+6Are ELECTRA's Sentence Embeddings Beyond Repair? The Case of Semantic Textual Similarity
While BERT produces high-quality sentence embeddings, its pre-training computational cost is a significant drawback. In contrast, ELECTRA provides a cost-effective pre-training objective and downstream task performance i…
Semantic Textual SimilaritySentenceSentence EmbeddingsSTS+2L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models
Fine-tuning pre-trained foundational language models (FLM) for specific tasks is often impractical, especially for resource-constrained devices. This necessitates the development of a Lifelong Learning (L3) framework tha…
Language ModelingLanguage ModellingLifelong learningSTS+2JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its Applications
Contrastive learning is widely used for sentence representation learning. Despite this prevalence, most studies have focused exclusively on English and few concern domain adaptation for domain-specific downstream tasks, …
Contrastive LearningDomain AdaptationInformation RetrievalLanguage Modelling+7Finetuning Transformer Models to Build ASAG System
Research towards creating systems for automatic grading of student answers to quiz and exam questions in educational settings has been ongoing since 1966. Over the years, the problem was divided into many categories. Amo…
STSSTS BenchmarkPatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT
This study provides an efficient approach for using text data to calculate patent-to-patent (p2p) technological similarity, and presents a hybrid framework for leveraging the resulting p2p similarity for applications suc…
ClassificationGeneral ClassificationMulti-Label ClassificationPatent classification+5Natural Language Understanding for Argumentative Dialogue Systems in the Opinion Building Domain
This paper introduces a natural language understanding (NLU) framework for argumentative dialogue systems in the information-seeking and opinion building domain. The proposed framework consists of two sub-models, namely …
Natural Language UnderstandingSTSSTS BenchmarkEvaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks
Contextualized representations from a pre-trained language model are central to achieve a high performance on downstream NLP task. The pre-trained BERT and A Lite BERT (ALBERT) models can be fine-tuned to give state-ofth…
Language ModellingNatural Language InferenceSemantic Textual SimilaritySentence+5Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity
We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings. We apply, extend and evaluate different meta-embedding methods from…
Dimensionality ReductionSemantic Textual SimilaritySentenceSTS+1Fast and Discriminative Semantic Embedding
The embedding of words and documents in compact, semantically meaningful vector spaces is a crucial part of modern information systems. Deep Learning models are powerful but their hyperparameter selection is often comple…
STSSTS BenchmarkParaphrase Thought: Sentence Embedding Module Imitating Human Language Recognition
Sentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a sentence in order to achieve an enhanced…
Document ClassificationGeneral ClassificationMachine TranslationParaphrase Identification+7Sentence Modeling via Multiple Word Embeddings and Multi-level Comparison for Semantic Textual Similarity
Different word embedding models capture different aspects of linguistic properties. This inspired us to propose a model (M-MaxLSTM-CNN) for employing multiple sets of word embeddings for evaluating sentence similarity/re…
Natural Language InferenceRelationSemantic Textual SimilaritySentence+7Learning Semantic Textual Similarity from Conversations
We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversational input-response pairs. The resulting…
Community Question AnsweringNatural Language InferenceQuestion AnsweringQuestion Similarity+6SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation
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, di…
Machine TranslationNatural Language InferenceQuestion AnsweringSemantic Textual Similarity+3DT\_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output
We 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 scores …
LemmatizationSemantic SimilaritySemantic Textual SimilaritySentence+4SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
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, di…
Machine TranslationQuestion AnsweringSemantic Textual SimilaritySTS+2