REVIEW 4 cited by
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed representations of sentences. Our method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings.
Forward citations
Cited by 4 Pith papers
-
How Sequence-to-Sequence Models Perceive Language Styles?
Style in text is represented by the covariance matrix of seq2seq semantic vectors, enabling a whitening-coloring style transfer algorithm.
-
Hamming Sentence Embeddings for Information Retrieval
A neural compressor turns sentence embeddings into binary codes that retain semantic similarity performance on STS benchmarks while cutting memory by up to 256:1.
-
When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey
A systematic literature survey of 61 deep-learning-based IRBL studies that proposes taxonomies and a performance overview, concluding that DL mitigates lexical gap, code structure, and cold-start issues while LLM-base...
-
BioBridge: Unified Bio-Embedding with Bridging Modality in Code-Switched EMR
BioBridge improves emergency triage classification on Korean-English code-switched EMRs by adding language segment tokens and BioSent2Vec medical features to transformer encoders, with modest gains over baselines.
Discussion (0). Continue with ORCID to comment.