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Impact of COVID-19 in Natural Language Processing Publications: a Disaggregated Study in Gender, Contribution and Experience
This study sheds light on the effects of COVID-19 in the particular field of Computational Linguistics and Natural Language Processing within Artificial Intelligence.
Multi-Task Learning for Improving Gender Accuracy in Neural Machine Translation
Machine Translation is highly impacted by social biases present in data sets, indicating that it reflects and amplifies stereotypes.
The TALP-UPC Participation in WMT21 News Translation Task: an mBART-based NMT Approach
We fine-tune mBART50 using the filtered data, and additionally, we train a Transformer model on the same data from scratch.
Impact of Gender Debiased Word Embeddings in Language Modeling
Gender, race and social biases have recently been detected as evident examples of unfairness in applications of Natural Language Processing.
Gender Bias in Multilingual Neural Machine Translation: The Architecture Matters
Multilingual Neural Machine Translation architectures mainly differ in the amount of sharing modules and parameters among languages.
Evaluating Gender Bias in Speech Translation
WinoST is the speech version of WinoMT which is a MT challenge set and both follow an evaluation protocol to measure gender accuracy.
Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information
Gender bias negatively impacts many natural language processing applications, including machine translation (MT).
The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT
In this article, we describe the TALP-UPC research group participation in the WMT19 news translation shared task for Kazakh-English.
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings
Gender bias is highly impacting natural language processing applications.
