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Found 9 papers, 2 papers with code
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations
While state-of-the-art NLP models have demonstrated excellent performance for aspect based sentiment analysis (ABSA), substantial evidence has been presented on their lack of robustness.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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A call for better unit testing for invariant risk minimisation
In this paper we present a controlled study on the linearized IRM framework (IRMv1) introduced in Arjovsky et al. (2020).
Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing
While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications.
Symbolic Priors for RNN-based Semantic Parsing
Seq2seq models based on Recurrent Neural Networks (RNNs) have recently received a lot of attention in the domain of Semantic Parsing for Question Answering.
Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge
We introduce LL-RNNs (Log-Linear RNNs), an extension of Recurrent Neural Networks that replaces the softmax output layer by a log-linear output layer, of which the softmax is a special case.
Move from Perturbed scheme to exponential weighting average
In an online decision problem, one makes decisions often with a pool of decision sequence called experts but without knowledge of the future.
