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    <abstract>We present FELIX – a flexible text-editing approach for generation, designed to derive maximum benefit from the ideas of decoding with bi-directional contexts and self-supervised pretraining. In contrast to conventional sequenceto-sequence (seq2seq) models, FELIX is efficient in low-resource settings and fast at inference time, while being capable of modeling flexible input-output transformations. We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input. The tagging model employs a novel Pointer mechanism, while the insertion model is based on a Masked Language Model (MLM). Both of these models are chosen to be non-autoregressive to guarantee faster inference. FELIX performs favourably when compared to recent text-editing methods and strong seq2seq baselines when evaluated on four NLG tasks: Sentence Fusion, Machine Translation Automatic Post-Editing, Summarization, and Text Simplification</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.111</identifier>
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%0 Conference Proceedings
%T FELIX: Flexible Text Editing Through Tagging and Insertion
%A Mallinson, Jonathan
%A Severyn, Aliaksei
%A Malmi, Eric
%A Garrido, Guillermo
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F mallinson-etal-2020-felix
%X We present FELIX – a flexible text-editing approach for generation, designed to derive maximum benefit from the ideas of decoding with bi-directional contexts and self-supervised pretraining. In contrast to conventional sequenceto-sequence (seq2seq) models, FELIX is efficient in low-resource settings and fast at inference time, while being capable of modeling flexible input-output transformations. We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input. The tagging model employs a novel Pointer mechanism, while the insertion model is based on a Masked Language Model (MLM). Both of these models are chosen to be non-autoregressive to guarantee faster inference. FELIX performs favourably when compared to recent text-editing methods and strong seq2seq baselines when evaluated on four NLG tasks: Sentence Fusion, Machine Translation Automatic Post-Editing, Summarization, and Text Simplification
%R 10.18653/v1/2020.findings-emnlp.111
%U https://aclanthology.org/2020.findings-emnlp.111/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.111
%P 1244-1255

Markdown (Informal)

[FELIX: Flexible Text Editing Through Tagging and Insertion](https://aclanthology.org/2020.findings-emnlp.111/) (Mallinson et al., Findings 2020)

• FELIX: Flexible Text Editing Through Tagging and Insertion (Mallinson et al., Findings 2020)

ACL

• Jonathan Mallinson, Aliaksei Severyn, Eric Malmi, and Guillermo Garrido. 2020. FELIX: Flexible Text Editing Through Tagging and Insertion. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1244–1255, Online. Association for Computational Linguistics.