activity
20182022
most citedThe GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

52 citations · 58 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20223 cited

Efficient Machine Translation Domain Adaptation

Pedro Henrique Martins, Zita Marinho, André F. T. Martins

Machine translation models struggle when translating out-of-domain text, which makes domain adaptation a topic of critical importance. However, most domain adaptation methods focus…

cs.CL202152 cited

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…

cs.CL2020

Sparse Text Generation

Pedro Henrique Martins, Zita Marinho, André F. T. Martins

Current state-of-the-art text generators build on powerful language models such as GPT-2, achieving impressive performance. However, to avoid degenerate text, they require sampling…

cs.CL20193 cited

Joint Learning of Named Entity Recognition and Entity Linking

Pedro Henrique Martins, Zita Marinho, André F. T. Martins

Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected. Howeve…

cs.CL2018

A deep learning approach for understanding natural language commands for mobile service robots

Pedro Henrique Martins, Luís Custódio, Rodrigo Ventura

Using natural language to give instructions to robots is challenging, since natural language understanding is still largely an open problem. In this paper we address this problem b…