Papers Paraphrase Identification
“Paraphrase Identification” 태그가 달린 논문 174편 · 필터 해제
LeWiDi-2025 at NLPerspectives: Third Edition of the Learning with Disagreements Shared Task
Many researchers have reached the conclusion that AI models should be trained to be aware of the possibility of variation and disagreement in human judgments, and evaluated as per their ability to recognize such variatio…
Natural Language InferenceParaphrase IdentificationSarcasm DetectionCompressed Models are NOT Trust-equivalent to Their Large Counterparts
Large Deep Learning models are often compressed before being deployed in a resource-constrained environment. Can we trust the prediction of compressed models just as we trust the prediction of the original large model? E…
Natural Language InferenceParaphrase IdentificationText ClassificationEvaluating the Effectiveness of Linguistic Knowledge in Pretrained Language Models: A Case Study of Universal Dependencies
Universal Dependencies (UD), while widely regarded as the most successful linguistic framework for cross-lingual syntactic representation, remains underexplored in terms of its effectiveness. This paper addresses this ga…
Paraphrase IdentificationEnhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked Data
Paraphrasing re-expresses meaning to enhance applications like text simplification, machine translation, and question-answering. Specific paraphrase types facilitate accurate semantic analysis and robust language models.…
Machine TranslationParaphrase GenerationParaphrase IdentificationQuestion Answering+2Enhancing Plagiarism Detection in Marathi with a Weighted Ensemble of TF-IDF and BERT Embeddings for Low-Resource Language Processing
Plagiarism involves using another person's work or concepts without proper attribution, presenting them as original creations. With the growing amount of data communicated in regional languages such as Marathi -- one of …
Paraphrase IdentificationSentenceSentence EmbeddingsTougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks
Recent advancements in natural language processing have highlighted the vulnerability of deep learning models to adversarial attacks. While various defence mechanisms have been proposed, there is a lack of comprehensive …
Adversarial RobustnessBenchmarkingNatural Language InferenceParaphrase Identification+2Application Specific Compression of Deep Learning Models
Large Deep Learning models are compressed and deployed for specific applications. However, current Deep Learning model compression methods do not utilize the information about the target application. As a result, the com…
Deep LearningModel CompressionNatural Language InferenceParaphrase IdentificationCross-lingual paraphrase identification
The paraphrase identification task involves measuring semantic similarity between two short sentences. It is a tricky task, and multilingual paraphrase identification is even more challenging. In this work, we train a bi…
Cross-Lingual Paraphrase IdentificationParaphrase IdentificationSemantic SimilaritySemantic Textual SimilarityIs Modularity Transferable? A Case Study through the Lens of Knowledge Distillation
The rise of Modular Deep Learning showcases its potential in various Natural Language Processing applications. Parameter-efficient fine-tuning (PEFT) modularity has been shown to work for various use cases, from domain a…
Domain AdaptationKnowledge DistillationLanguage ModelingLanguage Modelling+6Memory-efficient Stochastic methods for Memory-based Transformers
Training Memory-based transformers can require a large amount of memory and can be quite inefficient. We propose a novel two-phase training mechanism and a novel regularization technique to improve the training efficienc…
Language ModelingLanguage ModellingParaphrase IdentificationSplitEE: Early Exit in Deep Neural Networks with Split Computing
Deep Neural Networks (DNNs) have drawn attention because of their outstanding performance on various tasks. However, deploying full-fledged DNNs in resource-constrained devices (edge, mobile, IoT) is difficult due to the…
Natural Language InferenceParaphrase IdentificationDo Multilingual Language Models Think Better in English?
Translate-test is a popular technique to improve the performance of multilingual language models. This approach works by translating the input into English using an external machine translation system, and running infere…
Common Sense ReasoningCross-Lingual Natural Language InferenceCross-Lingual Paraphrase IdentificationLanguage Modeling+6Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language Understanding
Multilingual pretrained language models (MPLMs) have demonstrated substantial performance improvements in zero-shot cross-lingual transfer across various natural language understanding tasks by finetuning MPLMs on task-s…
Cross-Lingual TransferNatural Language InferenceNatural Language UnderstandingParaphrase Identification+3Assessing Word Importance Using Models Trained for Semantic Tasks
Many NLP tasks require to automatically identify the most significant words in a text. In this work, we derive word significance from models trained to solve semantic task: Natural Language Inference and Paraphrase Ident…
Natural Language InferenceParaphrase IdentificationCross-functional Analysis of Generalisation in Behavioural Learning
In behavioural testing, system functionalities underrepresented in the standard evaluation setting (with a held-out test set) are validated through controlled input-output pairs. Optimising performance on the behavioural…
Paraphrase IdentificationReading ComprehensionSentiment AnalysisSMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases
Recent studies reveal that various biases exist in different NLP tasks, and over-reliance on biases results in models' poor generalization ability and low adversarial robustness. To mitigate datasets biases, previous wor…
Adversarial RobustnessNatural Language InferenceParaphrase IdentificationCo-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge
Semantic consistency recognition aims to detect and judge whether the semantics of two text sentences are consistent with each other. However, the existing methods usually encounter the challenges of synonyms, polysemy a…
Paraphrase IdentificationSentenceText MatchingParaphrase Identification with Deep Learning: A Review of Datasets and Methods
The rapid progress of Natural Language Processing (NLP) technologies has led to the widespread availability and effectiveness of text generation tools such as ChatGPT and Claude. While highly useful, these technologies a…
Paraphrase IdentificationText GenerationImproving word mover's distance by leveraging self-attention matrix
Measuring the semantic similarity between two sentences is still an important task. The word mover's distance (WMD) computes the similarity via the optimal alignment between the sets of word embeddings. However, WMD does…
Paraphrase IdentificationSemantic SimilaritySemantic Textual SimilaritySentence+1Scaling Instruction-Finetuned Language Models
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a partic…
Coreference ResolutionCross-Lingual Question AnsweringMMLUMulti-task Language Understanding+2