pwc-archive/papers-with-abstracts huggingface dataset:
HeLju@VarDial 2020: Social Media Variety Geolocation with BERT Models
Authors: Yves Scherrer, Nikola Ljubešić
Date: 2020-12-01 00:00:00
Proceeding: VarDial (COLING) 2020 12

This paper describes the Helsinki-Ljubljana contribution to the VarDial shared task on social media variety geolocation. Our solutions are based on the BERT Transformer models, the constrained versions of our models reaching 1st place in two subtasks and 3rd place in one subtask, while our unconstrained models outperform all the constrained systems by a large margin. We show in our analyses that Transformer-based models outperform traditional models by far, and that improvements obtained by pre-training models on large quantities of (mostly standard) text are significant, but not drastic, with single-language models also outperforming multilingual models. Our manual analysis shows that two types of signals are the most crucial for a (mis)prediction: named entities and dialectal features, both of which are handled well by our models.
A dual-encoding system for dialect classification
Authors: Petru Rebeja, Dan Cristea
Date: 2020-12-01 00:00:00
Proceeding: VarDial (COLING) 2020 12
Tasks: Classification, Dialect Identification

In this paper we present the architecture, processing pipeline and results of the ensemble model developed for Romanian Dialect Identification task. The ensemble model consists of two TF-IDF encoders and a deep learning model aimed together at classifying input samples based on the writing patterns which are specific to each of the two dialects. Although the model performs well on the training set, its performance degrades heavily on the evaluation set. The drop in performance is due to the design decision which makes the model put too much weight on presence/lack of textual marks when determining the sample label.
Experiments in Language Variety Geolocation and Dialect Identification
Authors: Tommi Jauhiainen, Heidi Jauhiainen, Krister Lindén
Date: 2020-12-01 00:00:00
Proceeding: VarDial (COLING) 2020 12
Tasks: Dialect Identification

In this paper we describe the systems we used when participating in the VarDial Evaluation Campaign organized as part of the 7th workshop on NLP for similar languages, varieties and dialects. The shared tasks we participated in were the second edition of the Romanian Dialect Identification (RDI) and the first edition of the Social Media Variety Geolocation (SMG). The submissions of our SUKI team used generative language models based on Naive Bayes and character n-grams.
Exploring the Power of Romanian BERT for Dialect Identification
Authors: George-Eduard Zaharia, Andrei-Marius Avram, Dumitru-Clementin Cercel, Traian Rebedea
Date: 2020-12-01 00:00:00
Proceeding: VarDial (COLING) 2020 12
Tasks: Dialect Identification, Opinion Mining

Dialect identification represents a key aspect for improving a series of tasks, for example, opinion mining, considering that the location of the speaker can greatly influence the attitude towards a subject. In this work, we describe the systems developed by our team for VarDial 2020: Romanian Dialect Identification, a task specifically created for challenging participants to solve the previously mentioned issue. More specifically, we introduce a series of neural systems based on Transformers, that combine a BERT model exclusively pre-trained on the Romanian language with techniques such as adversarial training or character-level embeddings. By using these approaches, we were able to obtain a 0.6475 macro F1 score on the test dataset, thus allowing us to be ranked 5th out of 8 participant teams.
ZHAW-InIT - Social Media Geolocation at VarDial 2020
Authors: Fernando Benites, Manuela Hürlimann, Pius von Däniken, Mark Cieliebak
Date: 2020-12-01 00:00:00
Proceeding: VarDial (COLING) 2020 12

We describe our approaches for the Social Media Geolocation (SMG) task at the VarDial Evaluation Campaign 2020. The goal was to predict geographical location (latitudes and longitudes) given an input text. There were three subtasks corresponding to German-speaking Switzerland (CH), Germany and Austria (DE-AT), and Croatia, Bosnia and Herzegovina, Montenegro and Serbia (BCMS). We submitted solutions to all subtasks but focused our development efforts on the CH subtask, where we achieved third place out of 16 submissions with a median distance of 15.93 km and had the best result of 14 unconstrained systems. In the DE-AT subtask, we ranked sixth out of ten submissions (fourth of 8 unconstrained systems) and for BCMS we achieved fourth place out of 13 submissions (second of 11 unconstrained systems).
Discriminating between standard Romanian and Moldavian tweets using filtered character ngrams
Authors: Andrea Ceolin, Hong Zhang
Date: 2020-12-01 00:00:00
Proceeding: VarDial (COLING) 2020 12
Tasks: Articles, text-classification, Text Classification

We applied word unigram models, character ngram models, and CNNs to the task of distinguishing tweets of two related dialects of Romanian (standard Romanian and Moldavian) for the VarDial 2020 RDI shared task (Gaman et al. 2020). The main challenge of the task was to perform cross-genre text classification: specifically, the models must be trained using text from news articles, and be used to predict tweets. Our best model was a Naive Bayes model trained on character ngrams, with the most common ngrams filtered out. We also applied SVMs and CNNs, but while they yielded the best performance on an evaluation dataset of news article, their accuracy significantly dropped when they were used to predict tweets. Our best model reached an F1 score of 0.715 on the evaluation dataset of tweets, and 0.667 on the held-out test dataset. The model ended up in the third place in the shared task.
Challenges in Neural Language Identification: NRC at VarDial 2020
Authors: Gabriel Bernier-Colborne, Cyril Goutte
Date: 2020-12-01 00:00:00
Proceeding: VarDial (COLING) 2020 12
Tasks: Language Identification

We describe the systems developed by the National Research Council Canada for the Uralic language identification shared task at the 2020 VarDial evaluation campaign. Although our official results were well below the baseline, we show in this paper that this was not due to the neural approach to language identification in general, but to a flaw in the function we used to sample data for training and evaluation purposes. Preliminary experiments conducted after the evaluation period suggest that our neural approach to language identification can achieve state-of-the-art results on this task, although further experimentation is required.
Geolocation of Tweets with a BiLSTM Regression Model
Authors: Piyush Mishra
Date: 2020-12-01 00:00:00
Proceeding: VarDial (COLING) 2020 12
Tasks: model, Recommendation Systems, regression

Identifying a user’s location can be useful for recommendation systems, demographic analyses, and disaster outbreak monitoring. Although Twitter allows users to voluntarily reveal their location, such information isn’t universally available. Analyzing a tweet can provide a general estimation of a tweet location while giving insight into the dialect of the user and other linguistic markers. Such linguistic attributes can be used to provide a regional approximation of tweet origins. In this paper, we present a neural regression model that can identify the linguistic intricacies of a tweet to predict the location of the user. The final model identifies the dialect embedded in the tweet and predicts the location of the tweet.
A Case Study of NLG from Multimedia Data Sources: Generating Architectural Landmark Descriptions
Authors: Simon Mille, Spyridon Symeonidis, Maria Rousi, Montserrat Marimon Felipe, Klearchos Stavrothanasopoulos, Petros Alvanitopoulos, Roberto Carlini Salguero, Jens Grivolla, Georgios Meditskos, Stefanos Vrochidis, Leo Wanner
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Retrieval

In this paper, we present a pipeline system that generates architectural landmark descriptions using textual, visual and structured data. The pipeline comprises five main components:(i) a textual analysis component, which extracts information from Wikipedia pages; (ii)a visual analysis component, which extracts information from copyright-free images; (iii) a retrieval component, which gathers relevant (property, subject, object) triples from DBpedia; (iv) a fusion component, which stores the contents from the different modalities in a Knowledge Base (KB) and resolves the conflicts that stem from using different sources of information; (v) an NLG component, which verbalises the resulting contents of the KB. We show that thanks to the addition of other modalities, we can make the verbalisation of DBpedia triples more relevant and/or inspirational.
OWLSIZ: An isiZulu CNL for structured knowledge validation
Authors: Zola Mahlaza, C. Maria Keet
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12

In iterative knowledge elicitation, engineers are expected to be directly involved in validating the already captured knowledge and obtaining new knowledge increments, thus making the process time consuming. Languages such as English have controlled natural languages than can be repurposed to generate natural language questions from an ontology in order to allow a domain expert to independently validate the contents of an ontology without understanding a ontology authoring language such as OWL. IsiZulu, South Africa’s main L1 language by number speakers, does not have such a resource, hence, it is not possible to build a verbaliser to generate such questions. Therefore, we propose an isiZulu controlled natural language, called OWL Simplified isiZulu (OWLSIZ), for producing grammatical and fluent questions from an ontology. Human evaluation of the generated questions showed that participants’ judgements agree that most (83%) questions are positive for grammaticality or understandability.
A General Benchmarking Framework for Text Generation
Authors: Diego Moussallem, Paramjot Kaur, Thiago Ferreira, Chris van der Lee, Anastasia Shimorina, Felix Conrads, Michael Röder, René Speck, Claire Gardent, Simon Mille, Nikolai Ilinykh, Axel-Cyrille Ngonga Ngomo
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Benchmarking, Knowledge Graphs, Text Generation

The RDF-to-text task has recently gained substantial attention due to the continuous growth of RDF knowledge graphs in number and size. Recent studies have focused on systematically comparing RDF-to-text approaches on benchmarking datasets such as WebNLG. Although some evaluation tools have already been proposed for text generation, none of the existing solutions abides by the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles and involves RDF data for the knowledge extraction task. In this paper, we present BENG, a FAIR benchmarking platform for Natural Language Generation (NLG) and Knowledge Extraction systems with focus on RDF data. BENG builds upon the successful benchmarking platform GERBIL, is opensource and is publicly available along with the data it contains.
Controllable Neural Natural Language Generation: comparison of state-of-the-art control strategies
Authors: Yuanmin Leng, François Portet, Cyril Labbé, Raheel Qader
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: TAG, Text Generation

Most NLG systems target text fluency and grammatical correctness, disregarding control over text structure and length. However, control over the output plays an important part in industrial NLG applications. In this paper, we study different strategies of control in triple-totext generation systems particularly from the aspects of text structure and text length. Regarding text structure, we present an approach that relies on aligning the input entities with the facts in the target side. It makes sure that the order and the distribution of entities in both the input and the text are the same. As for control over text length, we show two different approaches. One is to supply length constraint as input while the other is to force the end-ofsentence tag to be included at each step when using top-k decoding strategy. Finally, we propose four metrics to assess the degree to which these methods will affect a NLG system’s ability to control text structure and length. Our analyses demonstrate that all the methods enhance the system’s ability with a slight decrease in text fluency. In addition, constraining length at the input level performs much better than control at decoding level.
Enhancing Sequence-to-Sequence Modelling for RDF triples to Natural Text
Authors: Oriol Domingo, David Bergés, Roser Cantenys, Roger Creus, José A. R. Fonollosa
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Machine Translation, Translation

establishes key guidelines on how, which and when Machine Translation (MT) techniques are worth applying to RDF-to-Text task. Not only do we apply and compare the most prominent MT architecture, the Transformer, but we also analyze state-of-the-art techniques such as Byte Pair Encoding or Back Translation to demonstrate an improvement in generalization. In addition, we empirically show how to tailor these techniques to enhance models relying on learned embeddings rather than using pretrained ones. Automatic metrics suggest that Back Translation can significantly improve model performance up to 7 BLEU points, hence, opening a window for surpassing state-of-the-art results with appropriate architectures.
Utilising Knowledge Graph Embeddings for Data-to-Text Generation
Authors: Nivranshu Pasricha, Mihael Arcan, Paul Buitelaar
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Data-to-Text Generation, Knowledge Graph Embeddings, Text Generation

Data-to-text generation has recently seen a move away from modular and pipeline architectures towards end-to-end architectures based on neural networks. In this work, we employ knowledge graph embeddings and explore their utility for end-to-end approaches in a data-to-text generation task. Our experiments show that using knowledge graph embeddings can yield an improvement of up to 2 – 3 BLEU points for seen categories on the WebNLG corpus without modifying the underlying neural network architecture.
The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020)
Authors: Thiago castro Ferreira, Claire Gardent, Nikolai Ilinykh, Chris van der Lee, Simon Mille, Diego Moussallem, Anastasia Shimorina
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Semantic Parsing, Text Generation

WebNLG+ offers two challenges: (i) mapping sets of RDF triples to English or Russian text (generation) and (ii) converting English or Russian text to sets of RDF triples (semantic parsing). Compared to the eponymous WebNLG challenge, WebNLG+ provides an extended dataset that enable the training, evaluation, and comparison of microplanners and semantic parsers. In this paper, we present the results of the generation and semantic parsing task for both English and Russian and provide a brief description of the participating systems.
{\mathcal{P}^2}: A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation
Authors: Qipeng Guo, Zhijing Jin, Ning Dai, Xipeng Qiu, xiangyang xue, David Wipf, Zheng Zhang
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Knowledge Graphs, Text Generation

Text verbalization of knowledge graphs is an important problem with wide application to natural language generation (NLG) systems. It is challenging because the generated text not only needs to be grammatically correct (fluency), but also has to contain the given structured knowledge input (relevance) and meet some other criteria. We develop a plan-and-pretrain approach, \mathcal{P}^2, which consists of a relational graph convolutional network (RGCN) planner and the pretrained sequence-tosequence (Seq2Seq) model T5. Specifically, the R-GCN planner first generates an order of the knowledge graph triplets, corresponding to the order that they will be mentioned in text, and then T5 produces the surface realization of the given plan. In the WebNLG+ 2020 Challenge, our submission ranked in 1st place on all automatic and human evaluation criteria of the English RDF-to-text generation task.
Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task
Authors: Zixiaofan Yang, Arash Einolghozati, Hakan Inan, Keith Diedrick, Angela Fan, Pinar Donmez, Sonal Gupta
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12

Converting a knowledge graph or sub-graph to natural text is useful when answering questions based on a knowledge base. High-capacity language models pre-trained on large-scale text corpora have recently been shown to be powerful when fine-tuned for the knowledge-graph-to-text (KG-to-text) task. In this paper, we propose two classes of methods to improve such pre-trained models for this task. First, we improve the structure awareness of the model by organizing the input as well as learning optimal ordering via multitask learning. Second, we bridge the domain gap between text-to-text and KG-to-text tasks via a second-phase KG-to-text pre-training on similar datasets and extra lexicalization supervision to make the input more similar to natural text. We demonstrate the efficacy of our methods on the popular WebNLG dataset. Our best model achieves an almost 3 point BLEU improvement on a strong baseline while lowering the relative slot-error-rate by around 35%. We also validate our results via human evaluation.
Leveraging Large Pretrained Models for WebNLG 2020
Authors: Xintong Li, Aleksandre Maskharashvili, Symon Jory Stevens-Guille, Michael White
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Reranking

In this paper, we report experiments on finetuning large pretrained models to realize resource description framework (RDF) triples to natural language. We provide the details of how to build one of the top-ranked English generation models in WebNLG Challenge 2020. We also show that there appears to be considerable potential for reranking to improve the current state of the art both in terms of statistical metrics and model-based metrics. Our human analyses of the generated texts show that for Russian, pretrained models showed some success, both in terms of lexical and morpho-syntactic choices for generation, as well as for content aggregation. Nevertheless, in a number of cases, the model can be unpredictable, both in terms of failure or success. Omissions of the content and hallucinations, which in many cases occurred at the same time, were major problems. By contrast, the models for English showed near perfect performance on the validation set.
Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing
Authors: Oshin Agarwal, Mihir Kale, Heming Ge, Siamak Shakeri, Rami Al-Rfou
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Data-to-Text Generation, Machine Translation, Semantic Parsing, Text Generation, Translation

We present a system for bilingual Data-ToText Generation and Semantic Parsing. We use a text-to-text generator to learn a single model that works for both languages on each of the tasks. The model is aided by machine translation during both pre-training and fine-tuning. We evaluate the system on WebNLG 2020 data 1 , which consists of RDF triples in English and natural language sentences in English and Russian for both the tasks. We achieve considerable gains over monolingual models, especially on unseen relations and Russian.
NILC at WebNLG+: Pretrained Sequence-to-Sequence Models on RDF-to-Text Generation
Authors: Marco Antonio Sobrevilla Cabezudo, Thiago A. S. Pardo
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Data-to-Text Generation, Text Generation

This paper describes the submission by the NILC Computational Linguistics research group of the University of São Paulo/Brazil to the RDF-to-Text task for English at the WebNLG+ challenge. The success of the current pretrained models like BERT or GPT-2 in text-to-text generation tasks is well-known, however, its application/success on data-totext generation has not been well-studied and proven. This way, we explore how good a pretrained model, in particular BART, performs on the data-to-text generation task. The results obtained were worse than the baseline and other systems in almost all automatic measures. However, the human evaluation shows better results for our system. Besides, results suggest that BART may generate paraphrases of reference texts.
NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation
Authors: Nivranshu Pasricha, Mihael Arcan, Paul Buitelaar
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Text Generation, Transfer Learning

This paper describes the system submitted by NUIG-DSI to the WebNLG+ challenge 2020 in the RDF-to-text generation task for the English language. For this challenge, we leverage transfer learning by adopting the T5 model architecture for our submission and fine-tune the model on the WebNLG+ corpus. Our submission ranks among the top five systems for most of the automatic evaluation metrics achieving a BLEU score of 51.74 over all categories with scores of 58.23 and 45.57 across seen and unseen categories respectively.
RDFjsRealB: a Symbolic Approach for Generating Text from RDF Triples
Authors: Guy Lapalme
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Text Generation

This paper describes the Resource Description Framework (RDF) triples verbalizer developed for the WEB NLG CHALLENGE 2020 shared task. After reviewing representative works in Natural Language Generation in the context of the Semantic Web, the task is then described. We then sketch the symbolic approach we used for verbalizing RDF triples: once the triples are grouped by subject, each group is realized as one or more sentences using templates written in Python whose output is feed to an English realizer written in Javascript. The system was developed using the test data of the previous edition of the task and the train and development data of this year’s task. The automatic scores for this year’s test data are quite competitive. We conclude with a critical review of the data and discuss the suitability of this competition results in a wider Natural Language Generation setting.
Semantic Triples Verbalization with Generative Pre-Training Model
Authors: Pavel Blinov
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: model, Text Generation

The paper devoted to the problem of automatic text generation from RDF triples. This problem was formalized and proposed as a part of the 2020 WebNLG challenge. We describe our approach to the RDF-to-text generation task based on a neural network model with the Generative Pre-Training (GPT-2) architecture. In particular, we outline a way of base GPT-2 model conversion to a model with language and classification heads and discuss the text generation methods. To research the parameters’ influence on the end-task performance a series of experiments was carried out. We report the result metrics and conclude with possible improvement directions.
Text-to-Text Pre-Training Model with Plan Selection for RDF-to-Text Generation
Authors: Natthawut Kertkeidkachorn, Hiroya Takamura
Date: 2020-12-01 00:00:00
Proceeding: ACL (WebNLG, INLG) 2020 12
Tasks: Text Generation

We report our system description for the RDFto-Text task in English on the WebNLG 2020 Challenge. Our approach consists of two parts: 1) RDF-to-Text Generation Pipeline and 2) Plan Selection. RDF-to-Text Generation Pipeline is built on the state-of-the-art pretraining model, while Plan Selection helps decide the proper plan into the pipeline.