Sentence Similarity
65 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Sentence Similarity
| Trend | Dataset | Best Model | Paper | Code | Compare |
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BIOSSES
Most implemented papers
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
imatge-upc/vqa-2016-cvprw
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ACL 2016
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances.
IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner
Interpretable semantic textual similarity (iSTS) task adds a crucial explanatory layer to pairwise sentence similarity.
Neural Paraphrase Generation with Stacked Residual LSTM Networks
pushpendughosh/Stock-market-forecasting
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COLING 2016
To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation.
Learning Neural Word Salience Scores
bollegala/repseval
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SEMEVAL 2018
Measuring the salience of a word is an essential step in numerous NLP tasks.
Transparent, Efficient, and Robust Word Embedding Access with WOMBAT
We present WOMBAT, a Python tool which supports NLP practitioners in accessing word embeddings from code.
Retrieval-Based Neural Code Generation
In models to generate program source code from natural language, representing this code in a tree structure has been a common approach.
Fixing Translation Divergences in Parallel Corpora for Neural MT
jmcrego/similarity
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EMNLP 2018
Corpus-based approaches to machine translation rely on the availability of clean parallel corpora.
Evaluating Composition Models for Verb Phrase Elliptical Sentence Embeddings
Our results show that non-linear addition and a non-linear tensor-based composition outperform the naive non-compositional baselines and the linear models, and that sentence encoders perform well on sentence similarity, but not on verb disambiguation.
Natural Language Generation for Effective Knowledge Distillation
Knowledge distillation can effectively transfer knowledge from BERT, a deep language representation model, to traditional, shallow word embedding-based neural networks, helping them approach or exceed the quality of other heavyweight language representation models.
A Divide-and-Conquer Approach to the Summarization of Long Documents
With this approach we can decompose the problem of long document summarization into smaller and simpler problems, reducing computational complexity and creating more training examples, which at the same time contain less noise in the target summaries compared to the standard approach.
