@inproceedings{szanto-berend-2020-prosperamnet,
title = "{P}rosper{AM}net at {F}in{C}ausal 2020, Task 1 {\&} 2: Modeling causality in financial texts using multi-headed transformers",
author = "Sz{\'a}nt{\'o}, Zsolt and
Berend, G{\'a}bor",
editor = "El-Haj, Dr Mahmoud and
Athanasakou, Dr Vasiliki and
Ferradans, Dr Sira and
Salzedo, Dr Catherine and
Elhag, Dr Ans and
Bouamor, Dr Houda and
Litvak, Dr Marina and
Rayson, Dr Paul and
Giannakopoulos, Dr George and
Pittaras, Nikiforos",
booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "COLING",
url = "https://aclanthology.org/2020.fnp-1.13",
pages = "80--84",
abstract = "This paper introduces our efforts at the FinCasual shared task for modeling causality in financial utterances. Our approach uses the commonly and successfully applied strategy of fine-tuning a transformer-based language model with a twist, i.e. we modified the training and inference mechanism such that our model produces multiple predictions for the same instance. By designing such a model that returns k{\textgreater}1 predictions at the same time, we not only obtain a more resource efficient training (as opposed to fine-tuning some pre-trained language model k independent times), but our results indicate that we are also capable of obtaining comparable or even better evaluation scores that way. We compare multiple strategies for combining the k predictions of our model. Our submissions got ranked third on both subtasks of the shared task.",
}
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%0 Conference Proceedings
%T ProsperAMnet at FinCausal 2020, Task 1 & 2: Modeling causality in financial texts using multi-headed transformers
%A Szántó, Zsolt
%A Berend, Gábor
%Y El-Haj, Dr Mahmoud
%Y Athanasakou, Dr Vasiliki
%Y Ferradans, Dr Sira
%Y Salzedo, Dr Catherine
%Y Elhag, Dr Ans
%Y Bouamor, Dr Houda
%Y Litvak, Dr Marina
%Y Rayson, Dr Paul
%Y Giannakopoulos, Dr George
%Y Pittaras, Nikiforos
%S Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
%D 2020
%8 December
%I COLING
%C Barcelona, Spain (Online)
%F szanto-berend-2020-prosperamnet
%X This paper introduces our efforts at the FinCasual shared task for modeling causality in financial utterances. Our approach uses the commonly and successfully applied strategy of fine-tuning a transformer-based language model with a twist, i.e. we modified the training and inference mechanism such that our model produces multiple predictions for the same instance. By designing such a model that returns k\textgreater1 predictions at the same time, we not only obtain a more resource efficient training (as opposed to fine-tuning some pre-trained language model k independent times), but our results indicate that we are also capable of obtaining comparable or even better evaluation scores that way. We compare multiple strategies for combining the k predictions of our model. Our submissions got ranked third on both subtasks of the shared task.
%U https://aclanthology.org/2020.fnp-1.13
%P 80-84
Markdown (Informal)
[ProsperAMnet at FinCausal 2020, Task 1 & 2: Modeling causality in financial texts using multi-headed transformers](https://aclanthology.org/2020.fnp-1.13) (Szántó & Berend, FNP 2020)
ACL