Search Results for author:
Found 12 papers, 10 papers with code
ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse Paraphrasing
It relies on diverse paraphrasing: a conditional language model is first fine-tuned for paraphrasing, and diversity is later introduced at the decoding stage at each meta-learning episode.
ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning
It relies on diverse paraphrasing: a conditional language model is first fine-tuned for paraphrasing, and diversity is later introduced at the decoding stage at each meta-learning episode.
A Neural Few-Shot Text Classification Reality Check
Additionally, some models used in Computer Vision are yet to be tested in NLP applications.
Few-shot Pseudo-Labeling for Intent Detection
This performance is achieved on multiple intent detection datasets, even in more challenging situations where the number of classes is large or when the dataset is highly imbalanced.
Near-lossless Binarization of Word Embeddings
Word embeddings are commonly used as a starting point in many NLP models to achieve state-of-the-art performances.
Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata
While Wikipedia exists in 287 languages, its content is unevenly distributed among them.
Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types
We present a neural model for question generation from knowledge base triples in a "Zero-Shot" setup, that is generating questions for triples containing predicates, subject types or object types that were not seen at training time.
Unsupervised Open Relation Extraction
We explore methods to extract relations between named entities from free text in an unsupervised setting.
High Recall Open IE for Relation Discovery
Relation Discovery discovers predicates (relation types) from a text corpus relying on the co-occurrence of two named entities in the same sentence.
Dict2vec : Learning Word Embeddings using Lexical Dictionaries
Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks.
