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Found 7 papers, 2 papers with code
Learning to Diversify for Product Question Generation
For a given product description, our goal is to generate questions that reflect potential user information needs that are either missing or not well covered in the description.
Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce
In e-commerce, the watchlist enables users to track items over time and has emerged as a primary feature, playing an important role in users' shopping journey.
Scheduled Multi-Task Learning: From Syntax to Translation
Neural encoder-decoder models of machine translation have achieved impressive results, while learning linguistic knowledge of both the source and target languages in an implicit end-to-end manner.
From Raw Text to Universal Dependencies - Look, No Tags!
We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies.
Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing.
Ranked #3 on
Chinese Dependency Parsing
on Chinese Pennbank
Easy-First Dependency Parsing with Hierarchical Tree LSTMs
We suggest a compositional vector representation of parse trees that relies on a recursive combination of recurrent-neural network encoders.
