no code implementations • 16 Dec 2022 • Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, Aliaksei Severyn
Chain of thought prompting successfully improves the reasoning capabilities of large language models, achieving state of the art results on a range of datasets.
no code implementations • NAACL (ACL) 2022 • Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adamek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, Aliaksei Severyn
Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and style transfer.
no code implementations • 24 May 2022 • Jonathan Mallinson, Jakub Adamek, Eric Malmi, Aliaksei Severyn
This is achieved by decomposing the generation process into three sub-tasks: (1) tagging to decide on the subset of input tokens to be preserved in the output, (2) re-ordering to define their order in the output text, and (3) insertion to infill the missing tokens that are not present in the input.
1 code implementation • NeurIPS 2021 • Sachin Kumar, Eric Malmi, Aliaksei Severyn, Yulia Tsvetkov
As large-scale language model pretraining pushes the state-of-the-art in text generation, recent work has turned to controlling attributes of the text such models generate.
1 code implementation • ACL 2021 • Sascha Rothe, Jonathan Mallinson, Eric Malmi, Sebastian Krause, Aliaksei Severyn
This paper presents a simple recipe to train state-of-the-art multilingual Grammatical Error Correction (GEC) models.
Ranked #1 on Grammatical Error Correction on Falko-MERLIN (using extra training data)
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Eyal Ben-David, Orgad Keller, Eric Malmi, Idan Szpektor, Roi Reichart
Sentence fusion is the task of joining related sentences into coherent text.
no code implementations • EMNLP 2020 • Eric Malmi, Aliaksei Severyn, Sascha Rothe
This allows us to identify the source tokens to delete to transform the source text to match the style of the target domain.
no code implementations • INLG (ACL) 2020 • Nikola I. Nikolov, Eric Malmi, Curtis G. Northcutt, Loreto Parisi
The ability to combine symbols to generate language is a defining characteristic of human intelligence, particularly in the context of artistic story-telling through lyrics.
2 code implementations • Findings of the Association for Computational Linguistics 2020 • Jonathan Mallinson, Aliaksei Severyn, Eric Malmi, Guillermo Garrido
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.
5 code implementations • IJCNLP 2019 • Eric Malmi, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, Aliaksei Severyn
We propose LaserTagger - a sequence tagging approach that casts text generation as a text editing task.
Ranked #1 on Sentence Fusion on DiscoFuse
2 code implementations • NAACL 2019 • Mor Geva, Eric Malmi, Idan Szpektor, Jonathan Berant
We author a set of rules for identifying a diverse set of discourse phenomena in raw text, and decomposing the text into two independent sentences.
no code implementations • 13 Oct 2018 • Federica Calanca, Luiza Sayfullina, Lara Minkus, Claudia Wagner, Eric Malmi
Our work shows that soft skills can serve as partial predictors of the gender composition in job categories and that not all soft skills receive equal wage returns at the labour market.
2 code implementations • 20 Jul 2018 • Luiza Sayfullina, Eric Malmi, Juho Kannala
The disambiguation is formulated as a binary text classification problem where the prediction is made for the potential soft skill based on the context where it occurs.
no code implementations • WS 2017 • Sebastian Krause, Mikhail Kozhevnikov, Eric Malmi, Daniele Pighin
Conversational agents offer users a natural-language interface to accomplish tasks, entertain themselves, or access information.
no code implementations • 18 Jul 2017 • Luiza Sayfullina, Eric Malmi, Yiping Liao, Alex Jung
We propose a novel method for classifying resume data of job applicants into 27 different job categories using convolutional neural networks.
1 code implementation • LREC 2018 • Eric Malmi, Daniele Pighin, Sebastian Krause, Mikhail Kozhevnikov
We formulate the task of discourse connective prediction and release a dataset of 2. 9M sentence pairs separated by discourse connectives for this task.
1 code implementation • 29 Feb 2016 • Eric Malmi, Ingmar Weber
Our work addresses this need by studying the predictability of user demographics based on the list of a user's apps which is readily available to many app developers.
Social and Information Networks
1 code implementation • 18 May 2015 • Eric Malmi, Pyry Takala, Hannu Toivonen, Tapani Raiko, Aristides Gionis
First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines.