8 citations · 27 across the 9 of their papers we have counts for
10 papers · 1 filter
Transformers as Graph-to-Graph Models
James Henderson, Alireza Mohammadshahi, Andrei C. Coman +1
We argue that Transformers are essentially graph-to-graph models, with sequences just being a special case. Attention weights are functionally equivalent to graph edges. Our Graph-…
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question
Alireza Mohammadshahi, Thomas Scialom, Majid Yazdani +4
Existing metrics for evaluating the quality of automatically generated questions such as BLEU, ROUGE, BERTScore, and BLEURT compare the reference and predicted questions, providing…
SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages
Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard +3
In recent years, multilingual machine translation models have achieved promising performance on low-resource language pairs by sharing information between similar languages, thus e…
Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers
Luis Espinosa-Anke, Alexander Shvets, Alireza Mohammadshahi +2
Recognizing and categorizing lexical collocations in context is useful for language learning, dictionary compilation and downstream NLP. However, it is a challenging task due to th…
What Do Compressed Multilingual Machine Translation Models Forget?
Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard +3
Recently, very large pre-trained models achieve state-of-the-art results in various natural language processing (NLP) tasks, but their size makes it more challenging to apply them…
The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task
James Barry, Alireza Mohammadshahi, Joachim Wagner +2
We describe the DCU-EPFL submission to the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies. The task involves parsing Enhanced UD graphs, which are an extensi…
Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling
Alireza Mohammadshahi, James Henderson
Recent models have shown that incorporating syntactic knowledge into the semantic role labelling (SRL) task leads to a significant improvement. In this paper, we propose Syntax-awa…
Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement
Alireza Mohammadshahi, James Henderson
We propose the Recursive Non-autoregressive Graph-to-Graph Transformer architecture (RNGTr) for the iterative refinement of arbitrary graphs through the recursive application of a…
Graph-to-Graph Transformer for Transition-based Dependency Parsing
Alireza Mohammadshahi, James Henderson
We propose the Graph2Graph Transformer architecture for conditioning on and predicting arbitrary graphs, and apply it to the challenging task of transition-based dependency parsing…
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task
Alireza Mohammadshahi, Remi Lebret, Karl Aberer
In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objec…