Papers Sentence-Pair Classification
“Sentence-Pair Classification” 태그가 달린 논문 38편 · 필터 해제
On the effectiveness of Large Language Models in the mechanical design domain
In this work, we seek to understand the performance of large language models in the mechanical engineering domain. We leverage the semantic data found in the ABC dataset, specifically the assembly names that designers as…
ClassificationSentenceSentence-Pair Classificationzero-shot-classification+1Can linguists better understand DNA?
Multilingual transfer ability, which reflects how well models fine-tuned on one source language can be applied to other languages, has been well studied in multilingual pre-trained models. However, the existence of such …
ClassificationSentenceSentence-Pair ClassificationSentence SimilarityGenerating Synthetic Datasets for Few-shot Prompt Tuning
A major limitation of prompt tuning is its dependence on large labeled training datasets. Under few-shot learning settings, prompt tuning lags far behind full-model fine-tuning, limiting its scope of application. In this…
Few-Shot LearningMRPCQQPSentence+2New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in French
This paper introduces DACCORD, an original dataset in French for automatic detection of contradictions between sentences. It also presents new, manually translated versions of two datasets, namely the well known dataset …
Natural Language InferenceRTESentenceSentence-Pair Classification+1A Generic NLI approach for Classification of Sentiment Associated with Therapies
This paper describes our system for addressing SMM4H 2023 Shared Task 2 on "Classification of sentiment associated with therapies (aspect-oriented)". In our work, we adopt an approach based on Natural language inference …
ClassificationNatural Language InferenceSentenceSentence-Pair Classification+1Link Prediction for Wikipedia Articles as a Natural Language Inference Task
Link prediction task is vital to automatically understanding the structure of large knowledge bases. In this paper, we present our system to solve this task at the Data Science and Advanced Analytics 2023 Competition "Ef…
ArticlesLink PredictionNatural Language InferencePrediction+2DACCORD : un jeu de données pour la Détection Automatique d'énonCés COntRaDictoires en français
La tâche de détection automatique de contradictions logiques entre énoncés en TALN est une tâche de classification binaire, où chaque paire de phrases reçoit une étiquette selon que les deux phrases se contredisent ou no…
Binary ClassificationBinary text classificationSentenceSentence-Pair Classification+1mPMR: A Multilingual Pre-trained Machine Reader at Scale
We present multilingual Pre-trained Machine Reader (mPMR), a novel method for multilingual machine reading comprehension (MRC)-style pre-training. mPMR aims to guide multilingual pre-trained language models (mPLMs) to pe…
ClassificationMachine Reading ComprehensionNatural Language UnderstandingReading Comprehension+2Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs
This paper explores the use of text data augmentation techniques to enhance conflict and duplicate detection in software engineering tasks through sentence pair classification. The study adapts generic augmentation techn…
Data AugmentationLEMMASentenceSentence-Pair Classification+1Transfer learning for conflict and duplicate detection in software requirement pairs
Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software development processes by automatically identi…
SentenceSentence-Pair ClassificationTransfer LearningFine-mixing: Mitigating Backdoors in Fine-tuned Language Models
Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks. In Natural Language Processing (NLP), DNNs are often backdoored during the fine-tuning process of a large-scale Pre-trained Language Model (PLM)…
Language ModellingSentenceSentence-Pair ClassificationSentiment Analysis+2YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic
In this paper, we (a YNU-HPCC team) describe the system we built in the SemEval-2022 competition. As participants in Task 6 (titled “iSarcasmEval: Intended Sarcasm Detection In English and Arabic”), we implement the sent…
Binary ClassificationClassificationSarcasm DetectionSentence+1Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules
Argumentation mining is a growing area of research and has several interesting practical applications of mining legal arguments. Support and Attack relations are the backbone of any legal argument. However, there is no p…
SentenceSentence-Pair ClassificationMulti-label topic classification for COVID-19 literature with Bioformer
We describe Bioformer team's participation in the multi-label topic classification task for COVID-19 literature (track 5 of BioCreative VII). Topic classification is performed using different BERT models (BioBERT, PubMed…
ArticlesClassificationSentenceSentence-Pair Classification+1CBLUE: A Chinese Biomedical Language Understanding EvaluationBenchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually offering great promise for medical practice. With the development of biomedical language understanding bench…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Natural Language Understanding+2Sparse Distillation: Speeding Up Text Classification by Using Bigger Student Models
Distilling state-of-the-art transformer models into lightweight student models is an effective way to reduce computation cost at inference time. The student models are typically compact transformers with fewer parameters…
ClassificationDomain GeneralizationPrivacy PreservingSentence+3Revisiting Self-Training for Few-Shot Learning of Language Model
As unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. The question is how to effectively make use of such data. In this work, we revisit the self-training…
BenchmarkingFew-Shot LearningLanguage ModelingLanguage Modelling+5Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information
Entity relation extraction plays an important role in the biomedical, healthcare, and clinical research areas. Recently, pre-trained models based on transformer architectures and their variants have shown remarkable perf…
RelationRelation ExtractionSentenceSentence-Pair Classification+1Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem. In this work, we demonstrate that, d…
Language ModelingLanguage ModellingSentenceSentence-Pair ClassificationUnsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference
While recent research on natural language inference has considerably benefited from large annotated datasets, the amount of inference-related knowledge (including commonsense) provided in the annotated data is still rath…
Natural Language InferenceSentenceSentence-Pair ClassificationUnsupervised Pre-training