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Showing 251–275 of 334 papers

TitleStatusHype
Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model—0
Neural association between musical features and shared emotional perception while movie-watching: fMRI study—0
Neural representation of action sequences: how far can a simple snippet-matching model take us?—0
Noisy Text Data: Achilles’ Heel of BERT—0
Non-Autoregressive Neural Dialogue Generation—0
Non-Interchangeability between Heart Rate Variability and Pulse Rate Variability During Supine-to-Stand Tests—0
On Adversarial Examples for Biomedical NLP Tasks—0
On the Dimensionality of Sentence Embeddings—0
On the (im)possibility of sustainable artificial intelligence. Why it does not make sense to move faster when heading the wrong way—0
OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity—0
Parallel Bayesian Optimization Using Satisficing Thompson Sampling for Time-Sensitive Black-Box Optimization—0
Paraphrase Generation as Unsupervised Machine Translation—0
Patterns of social mobility across social groups in India—0
pH/T duality - wall properties and time evolution of plant cells—0
Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning—0
Predicting the Semantic Textual Similarity with Siamese CNN and LSTM—0
PromptBERT: Improving BERT Sentence Embeddings with Prompts—0
PropNet: a White-Box and Human-Like Network for Sentence Representation—0
ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language—0
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings—0
Rethinking STS and NLI in Large Language Models—0
Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS—0
Robots Enact Malignant Stereotypes—0
RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics—0
RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity—0
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