Sentence Embedding
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A Structured Self-attentive Sentence Embedding
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.
NATURAL LANGUAGE INFERENCE SENTENCE EMBEDDING SENTIMENT ANALYSIS
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks
• facebookresearch/InferSent
• 
The analysis sheds light on the relative strengths of different sentence embedding methods with respect to these low level prediction tasks, and on the effect of the encoded vector's dimensionality on the resulting representations.
Evaluation of sentence embeddings in downstream and linguistic probing tasks
• allenai/bilm-tf
• 
Despite the fast developmental pace of new sentence embedding methods, it is still challenging to find comprehensive evaluations of these different techniques.
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
• taoshen58/DiSAN
•
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively.
#31 best model for
Natural Language Inference
on SNLI
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.
Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations
• UKPLab/arxiv2018-xling-sentence-embeddings
•
Here, we generalize the concept of average word embeddings to power mean word embeddings.
Sentence Embeddings in NLI with Iterative Refinement Encoders
We can show that the sentence embeddings learned in this way can be utilized in a wide variety of transfer learning tasks, outperforming InferSent on 7 out of 10 and SkipThought on 8 out of 9 SentEval sentence embedding evaluation tasks.
#4 best model for
Natural Language Inference
on SciTail
NATURAL LANGUAGE INFERENCE SENTENCE EMBEDDING TRANSFER LEARNING
Dependency-based Convolutional Neural Networks for Sentence Embedding
In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies.
Word Mover's Embedding: From Word2Vec to Document Embedding
While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings.
DOCUMENT EMBEDDING SENTENCE EMBEDDING TEXT CLASSIFICATION WORD EMBEDDINGS
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator
COLING 2018
• badripatro/PQG
•
One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far.
MACHINE READING COMPREHENSION MACHINE TRANSLATION PARAPHRASE GENERATION SENTENCE EMBEDDING SENTIMENT ANALYSIS