This paper describes the participation of the BSC team in the WMT2021's Multilingual Low-Resource Translation for Indo-European Languages Shared Task. The system aims to solve the Subtask 2: Wikipedia cultural heritage articles, which involves transl
ation in four Romance languages: Catalan, Italian, Occitan and Romanian. The submitted system is a multilingual semi-supervised machine translation model. It is based on a pre-trained language model, namely XLM-RoBERTa, that is later fine-tuned with parallel data obtained mostly from OPUS. Unlike other works, we only use XLM to initialize the encoder and randomly initialize a shallow decoder. The reported results are robust and perform well for all tested languages.
This paper describes Charles University sub-mission for Terminology translation shared task at WMT21. The objective of this task is to design a system which translates certain terms based on a provided terminology database, while preserving high over
all translation quality. We competed in English-French language pair. Our approach is based on providing the desired translations alongside the input sentence and training the model to use these provided terms. We lemmatize the terms both during the training and inference, to allow the model to learn how to produce correct surface forms of the words, when they differ from the forms provided in the terminology database.
We present the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. This shared task comprises three binary classification subtasks with the goal to identify: toxic comments, engaging comments, and comments
that include indications of a need for fact-checking, here referred to as fact-claiming comments. Building on the two previous GermEval shared tasks on the identification of offensive language in 2018 and 2019, we extend this year's task definition to meet the demand of moderators and community managers to also highlight comments that foster respectful communication, encourage in-depth discussions, and check facts that lines of arguments rely on. The dataset comprises 4,188 posts extracted from the Facebook page of a German political talk show of a national public television broadcaster. A theoretical framework and additional reliability tests during the data annotation process ensure particularly high data quality. The shared task had 15 participating teams submitting 31 runs for the subtask on toxic comments, 25 runs for the subtask on engaging comments, and 31 for the subtask on fact-claiming comments. The shared task website can be found at https://germeval2021toxic.github.io/SharedTask/.
This paper describes a system proposed for the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (EUD). We propose a Graph Rewriting based system for computing Enhanced Universal Dependencies, given the Basic Universal Dependencies (UD).
We present the system submission from the FASTPARSE team for the EUD Shared Task at IWPT 2021. We engaged in the task last year by focusing on efficiency. This year we have focused on experimenting with new ideas on a limited time budget. Our system
is based on splitting the EUD graph into several trees, based on linguistic criteria. We predict these trees using a sequence-labelling parser and combine them into an EUD graph. The results were relatively poor, although not a total disaster and could probably be improved with some polishing of the system's rough edges.
We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that twe
et contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task 2 focused on censorship detection, and was offered in Chinese. A total of ten teams submitted systems for task 1, and one team participated in task 2; nine teams also submitted a system description paper. Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used. Most submissions achieved sizable improvements over several baselines, and the best systems used pre-trained Transformers and ensembles. The data, the scorers and the leaderboards for the tasks are available at http://gitlab.com/NLP4IF/nlp4if-2021.
