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Found 9 papers, 5 papers with code
Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study
The brittleness of finetuned language model performance on out-of-distribution (OOD) test samples in unseen domains has been well-studied for English, yet is unexplored for multi-lingual models.
Cross-Lingual Sentiment Classification
Sentiment Analysis
+3
IDAS: Intent Discovery with Abstractive Summarization
Intent discovery is the task of inferring latent intents from a set of unlabeled utterances, and is a useful step towards the efficient creation of new conversational agents.
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals
We demonstrate the effectiveness of our approach in sentiment classification, using IMDb data for training and other sets for OOD tests (i. e., Amazon, SemEval and Yelp).
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders
Powerful sentence encoders trained for multiple languages are on the rise.
Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering
In this paper, we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications.
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations.
Predefined Sparseness in Recurrent Sequence Models
Inducing sparseness while training neural networks has been shown to yield models with a lower memory footprint but similar effectiveness to dense models.
Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks
A DReLU, which comes with an unbounded positive and negative image, can be used as a drop-in replacement for a tanh activation function in the recurrent step of Quasi-Recurrent Neural Networks (QRNNs) (Bradbury et al. (2017)).
Improving Language Modeling using Densely Connected Recurrent Neural Networks
In this paper, we introduce the novel concept of densely connected layers into recurrent neural networks.
