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Found 17 papers, 1 papers with code
Interpreting Text Classifiers by Learning Context-sensitive Influence of Words
Many existing approaches for interpreting text classification models focus on providing importance scores for parts of the input text, such as words, but without a way to test or improve the interpretation method itself.
Efficient Classification of Long Documents Using Transformers
Several methods have been proposed for classifying long textual documents using Transformers.
Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics
Measuring bias is key for better understanding and addressing unfairness in NLP/ML models.
Neural Network based Extreme Classification and Similarity Models for Product Matching
Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience.
Creation of comparable corpora for English-Urdu, Arabic, Persian
To overcome the low availability of parallel resources the machine translation community has recognized the potential of using comparable resources as training data.
The USFD Spoken Language Translation System for IWSLT 2014
The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23. 45 and 14. 75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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An efficient and user-friendly tool for machine translation quality estimation
We present a new version of QUEST ― an open source framework for machine translation quality estimation ― which brings a number of improvements: (i) it provides a Web interface and functionalities such that non-expert users, e. g. translators or lay-users of machine translations, can get quality predictions (or internal features of the framework) for translations without having to install the toolkit, obtain resources or build prediction models; (ii) it significantly improves over the previous runtime performance by keeping resources (such as language models) in memory; (iii) it provides an option for users to submit the source text only and automatically obtain translations from Bing Translator; (iv) it provides a ranking of multiple translations submitted by users for each source text according to their estimated quality.
