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Found 11 papers, 5 papers with code
Measuring Robustness for NLP
The quality of Natural Language Processing (NLP) models is typically measured by the accuracy or error rate of a predefined test set.
Coding Textual Inputs Boosts the Accuracy of Neural Networks
Natural Language Processing (NLP) tasks are usually performed word by word on textual inputs.
ConceptX: A Framework for Latent Concept Analysis
We use an unsupervised method to discover concepts learned in these models and enable a graphical interface for humans to generate explanations for the concepts.
SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models
This paper describes the Stevens Institute of Technology's submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT).
Analyzing Encoded Concepts in Transformer Language Models
We propose a novel framework ConceptX, to analyze how latent concepts are encoded in representations learned within pre-trained language models.
Discovering Latent Concepts Learned in BERT
We address this limitation by discovering and analyzing latent concepts learned in neural network models in an unsupervised fashion and provide interpretations from the model's perspective.
Interpreting Criminal Charge Prediction and Its Algorithmic Bias via Quantum-Inspired Complex Valued Networks
Our analysis of the importance of each input feature shows the critical causal impact on decision-making, suggesting that criminal histories are statistically significant factors, while identifiers, such as race and age, are not.
Grouping Words with Semantic Diversity
Deep Learning-based NLP systems can be sensitive to unseen tokens and hard to learn with high-dimensional inputs, which critically hinder learning generalization.
A Clustering Framework for Lexical Normalization of Roman Urdu
Roman Urdu is an informal form of the Urdu language written in Roman script, which is widely used in South Asia for online textual content.
Diversity by Phonetics and its Application in Neural Machine Translation
We achieve significant and consistent improvements overall language pairs and datasets: French-English, German-English, and Chinese-English in medium task IWSLT'17 and French-English in large task WMT'18 Bio, with up to 4 BLEU points over the state-of-the-art.
CUNY-PKU Parser at SemEval-2019 Task 1: Cross-Lingual Semantic Parsing with UCCA
This paper describes the systems of the CUNY-PKU team in SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA.
