Search Results for author:
Found 13 papers, 11 papers with code
Leeroo Orchestrator: Elevating LLMs Performance Through Model Integration
In this paper, we propose an architecture to harness the collective knowledge of multiple trained LLMs to create a new state-of-the-art.
Ranked #4 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Investigating Multi-Pivot Ensembling with Massively Multilingual Machine Translation Models
Massively multilingual machine translation models allow for the translation of a large number of languages with a single model, but have limited performance on low- and very-low-resource translation directions.
Transformers as Graph-to-Graph Models
We argue that Transformers are essentially graph-to-graph models, with sequences just being a special case.
Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding
Experiments on the massively multilingual models M2M-100 (418M) and SMaLL-100 show that these methods suppress hallucinations and off-target translations, reducing the number of translations with segment-level chrF2 below 10 by 67-83% on average, and the number of translations with oscillatory hallucinations by 75-92% on average, across 57 tested translation directions.
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question
We demonstrate that RQUGE has a higher correlation with human judgment without relying on the reference question.
SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages
In recent years, multilingual machine translation models have achieved promising performance on low-resource language pairs by sharing information between similar languages, thus enabling zero-shot translation.
Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers
Recognizing and categorizing lexical collocations in context is useful for language learning, dictionary compilation and downstream NLP.
What Do Compressed Multilingual Machine Translation Models Forget?
In this work, we assess the impact of compression methods on Multilingual Neural Machine Translation models (MNMT) for various language groups, gender, and semantic biases by extensive analysis of compressed models on different machine translation benchmarks, i. e. FLORES-101, MT-Gender, and DiBiMT.
The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task
The task involves parsing Enhanced UD graphs, which are an extension of the basic dependency trees designed to be more facilitative towards representing semantic structure.
Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling
Recent models have shown that incorporating syntactic knowledge into the semantic role labelling (SRL) task leads to a significant improvement.
Ranked #6 on
Semantic Role Labeling
on CoNLL 2005
(using extra training data)
Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement
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 non-autoregressive Graph-to-Graph Transformer and apply it to syntactic dependency parsing.
Ranked #8 on
Dependency Parsing
on Penn Treebank
Graph-to-Graph Transformer for Transition-based Dependency Parsing
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
In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages.
