35 citations · 91 across the 9 of their papers we have counts for
10 papers
Type Diversity Enables Transformers to Generalise Compositionally
Anssi Moisio, Mathias Creutz, Mikko Kurimo
Compositional generalisation has been divided into lexical and structural generalisation. Previous work has found that structural generalisation is harder than lexical for Transfor…
LLMs' morphological analyses of complex FST-generated Finnish words
Anssi Moisio, Mathias Creutz, Mikko Kurimo
Rule-based language processing systems have been overshadowed by neural systems in terms of utility, but it remains unclear whether neural NLP systems, in practice, learn the gramm…
On Using Distribution-Based Compositionality Assessment to Evaluate Compositional Generalisation in Machine Translation
Anssi Moisio, Mathias Creutz, Mikko Kurimo
Compositional generalisation (CG), in NLP and in machine learning more generally, has been assessed mostly using artificial datasets. It is important to develop benchmarks to asses…
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran +74
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using…
Semantic Search as Extractive Paraphrase Span Detection
Jenna Kanerva, Hanna Kitti, Li-Hsin Chang +3
In this paper, we approach the problem of semantic search by framing the search task as paraphrase span detection, i.e. given a segment of text as a query phrase, the task is to id…
Grammatical Error Generation Based on Translated Fragments
Eetu Sjöblom, Mathias Creutz, Teemu Vahtola
We perform neural machine translation of sentence fragments in order to create large amounts of training data for English grammatical error correction. Our method aims at simulatin…