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Found 13 papers, 7 papers with code
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.
LM Transparency Tool: Interactive Tool for Analyzing Transformer Language Models
We present the LM Transparency Tool (LM-TT), an open-source interactive toolkit for analyzing the internal workings of Transformer-based language models.
Information Flow Routes: Automatically Interpreting Language Models at Scale
These routes can be represented as graphs where nodes correspond to token representations and edges to operations inside the network.
Neurons in Large Language Models: Dead, N-gram, Positional
Specifically, we focus on the OPT family of models ranging from 125m to 66b parameters and rely only on whether an FFN neuron is activated or not.
Automating Behavioral Testing in Machine Translation
Behavioral testing in NLP allows fine-grained evaluation of systems by examining their linguistic capabilities through the analysis of input-output behavior.
Explaining How Transformers Use Context to Build Predictions
Language Generation Models produce words based on the previous context.
Toxicity in Multilingual Machine Translation at Scale
We evaluate and analyze added toxicity when translating a large evaluation dataset (HOLISTICBIAS, over 472k sentences, covering 13 demographic axes) from English into 164 languages.
Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer
In Neural Machine Translation (NMT), each token prediction is conditioned on the source sentence and the target prefix (what has been previously translated at a decoding step).
On the Locality of Attention in Direct Speech Translation
Transformers have achieved state-of-the-art results across multiple NLP tasks.
Measuring the Mixing of Contextual Information in the Transformer
The Transformer architecture aggregates input information through the self-attention mechanism, but there is no clear understanding of how this information is mixed across the entire model.
Attention Weights in Transformer NMT Fail Aligning Words Between Sequences but Largely Explain Model Predictions
This work proposes an extensive analysis of the Transformer architecture in the Neural Machine Translation (NMT) setting.
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.
Improving accuracy and speeding up Document Image Classification through parallel systems
This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions.
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