Semantic Textual Similarity
283 papers with code • 11 benchmarks • 15 datasets
Semantic textual similarity deals with determining how similar two pieces of texts are. This can take the form of assigning a score from 1 to 5. Related tasks are paraphrase or duplicate identification.
Image source: Learning Semantic Textual Similarity from Conversations
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
Ranked #1 on Natural Language Inference on QNLI
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
Ranked #1 on Natural Language Inference on CommitmentBank
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
Ranked #1 on Question Answering on BoolQ
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging.
Ranked #9 on Semantic Textual Similarity on MRPC
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.
Ranked #3 on Question Answering on Story Cloze Test
In this paper, we propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model.
Ranked #2 on Paraphrase Identification on Quora Question Pairs