Miracle-dz/UniFAR-FTUs huggingface dataset:
The Early Modern Dutch Mediascape. Detecting Media Mentions in Chronicles Using Word Embeddings and CRF

While the production of information in the European early modern period is a well-researched topic, the question how people were engaging with the information explosion that occurred in early modern Europe, is still underexposed. This paper presents the annotations and experiments aimed at exploring whether we can automatically extract media related information (source, perception, and receiver) from a corpus of early modern Dutch chronicles in order to get insight in the mediascape of early modern middle class people from a historic perspective. In a number of classification experiments with Conditional Random Fields, three categories of features are tested: (i) raw and binary word embedding features, (ii) lexicon features, and (iii) character features. Overall, the classifier that uses raw embeddings performs slightly better. However, given that the best F-scores are around 0.60, we conclude that the machine learning approach needs to be combined with a close reading approach for the results to be useful to answer history research questions.
Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing

The training of NLP models often requires large amounts of labelled training data, which makes it difficult to expand existing models to new languages. While zero-shot cross-lingual transfer relies on multilingual word embeddings to apply a model trained on one language to another, Yarowski and Ngai (2001) propose the method of annotation projection to generate training data without manual annotation. This method was successfully used for the tasks of named entity recognition and coarse-grained entity typing, but we show that it is outperformed by zero-shot cross-lingual transfer when applied to the similar task of fine-grained entity typing. In our study of fine-grained entity typing with the FIGER type ontology for German, we show that annotation projection amplifies the English model’s tendency to underpredict level 2 labels and is beaten by zero-shot cross-lingual transfer on three novel test sets.
FrameNet-like Annotation of Olfactory Information in Texts

Although olfactory references play a crucial role in our cultural memory, only few works in NLP have tried to capture them from a computational perspective. Currently, the main challenge is not much the development of technological components for olfactory information extraction, given recent advances in semantic processing and natural language understanding, but rather the lack of a theoretical framework to capture this information from a linguistic point of view, as a preliminary step towards the development of automated systems. Therefore, in this work we present the annotation guidelines, developed with the help of history scholars and domain experts, aimed at capturing all the relevant elements involved in olfactory situations or events described in texts. These guidelines have been inspired by FrameNet annotation, but underwent some adaptations, which are detailed in this paper. Furthermore, we present a case study concerning the annotation of olfactory situations in English historical travel writings describing trips to Italy. An analysis of the most frequent role fillers show that olfactory descriptions pertain to some typical domains such as religion, food, nature, ancient past, poor sanitation, all supporting the creation of a stereotypical imagery related to Italy. On the other hand, positive feelings triggered by smells are prevalent, and contribute to framing travels to Italy as an exciting experience involving all senses.
Comparing Grammatical Theories of Code-Mixing

Code-mixed text generation systems have found applications in many downstream tasks, including speech recognition, translation and dialogue. A paradigm of these generation systems relies on well-defined grammatical theories of code-mixing, and there is a lack of comparison of these theories. We present a large-scale human evaluation of two popular grammatical theories, Matrix-Embedded Language (ML) and Equivalence Constraint (EC). We compare them against three heuristic-based models and quantitatively demonstrate the effectiveness of the two grammatical theories.
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese

High quality distributional models can capture lexical and semantic relations between words. Hence, researchers design various intrinsic tasks to test whether such relations are captured. However, most of the intrinsic tasks are designed for modern languages, and there is a lack of evaluation methods for distributional models of historical corpora. In this paper, we conducted BAHP: a benchmark of assessing word embeddings in Historical Portuguese, which contains four types of tests: analogy, similarity, outlier detection, and coherence. We examined word2vec models generated from two historical Portuguese corpora in these four test sets. The results demonstrate that our test sets are capable of measuring the quality of vector space models and can provide a holistic view of the model’s ability to capture syntactic and semantic information. Furthermore, the methodology for the creation of our test sets can be easily extended to other historical languages.
Comparison of methods for explicit discourse connective identification across various domains

Existing parse methods use varying approaches to identify explicit discourse connectives, but their performance has not been consistently evaluated in comparison to each other, nor have they been evaluated consistently on text other than newspaper articles. We here assess the performance on explicit connective identification of three parse methods (PDTB e2e, Lin et al., 2014; the winner of CONLL2015, Wang et al., 2015; and DisSent, Nie et al., 2019), along with a simple heuristic. We also examine how well these systems generalize to different datasets, namely written newspaper text (PDTB), written scientific text (BioDRB), prepared spoken text (TED-MDB) and spontaneous spoken text (Disco-SPICE). The results show that the e2e parser outperforms the other parse methods in all datasets. However, performance drops significantly from the PDTB to all other datasets. We provide a more fine-grained analysis of domain differences and connectives that prove difficult to parse, in order to highlight the areas where gains can be made.
Tackling Zero Pronoun Resolution and Non-Zero Coreference Resolution Jointly

Zero pronoun resolution aims at recognizing dropped pronouns and pointing out their anaphoric mentions, while non-zero coreference resolution targets at clustering mentions referring to the same entity. Existing efforts often deal with the two problems separately regardless of their close essential correlations. In this paper, we investigate the possibility of jointly solving zero pronoun resolution and coreference resolution via a novel end-to-end neural model. Specifically, we design a gap-masked self-attention model that encodes gaps and tokens in the same space, where gaps could capture valuable contextual information according to their surrounding tokens while tokens could maintain original sequential information without disturbance. Additionally, we also propose a two-stage interaction mechanism to make full use of the exclusive relationship between zero pronouns and mentions. Our empirical study conducted on the OntoNotes 5.0 Chinese dataset shows that our model could outperform corresponding state-of-the-art approaches on both tasks.
When does Further Pre-training MLM Help? An Empirical Study on Task-Oriented Dialog Pre-training

Further pre-training language models on in-domain data (domain-adaptive pre-training, DAPT) or task-relevant data (task-adaptive pre-training, TAPT) before fine-tuning has been shown to improve downstream tasks’ performances. However, in task-oriented dialog modeling, we observe that further pre-training MLM does not always boost the performance on a downstream task. We find that DAPT is beneficial in the low-resource setting, but as the fine-tuning data size grows, DAPT becomes less beneficial or even useless, and scaling the size of DAPT data does not help. Through Representational Similarity Analysis, we conclude that more data for fine-tuning yields greater change of the model’s representations and thus reduces the influence of initialization.
Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets

Finding informative COVID-19 posts in a stream of tweets is very useful to monitor health-related updates. Prior work focused on a balanced data setup and on English, but informative tweets are rare, and English is only one of the many languages spoken in the world. In this work, we introduce a new dataset of 5,000 tweets for finding informative COVID-19 tweets for Danish. In contrast to prior work, which balances the label distribution, we model the problem by keeping its natural distribution. We examine how well a simple probabilistic model and a convolutional neural network (CNN) perform on this task. We find a weighted CNN to work well but it is sensitive to embedding and hyperparameter choices. We hope the contributed dataset is a starting point for further work in this direction.
Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector

The application of predictive coding techniques to legal texts has the potential to greatly reduce the cost of legal review of documents, however, there is such a wide array of legal tasks and continuously evolving legislation that it is hard to construct sufficient training data to cover all cases. In this paper, we investigate few-shot and zero-shot approaches that require substantially less training data and introduce a triplet architecture, which for promissory statements produces performance close to that of a supervised system. This method allows predictive coding methods to be rapidly developed for new regulations and markets.
FANG-COVID: A New Large-Scale Benchmark Dataset for Fake News Detection in German

As the world continues to fight the COVID-19 pandemic, it is simultaneously fighting an ‘infodemic’ – a flood of disinformation and spread of conspiracy theories leading to health threats and the division of society. To combat this infodemic, there is an urgent need for benchmark datasets that can help researchers develop and evaluate models geared towards automatic detection of disinformation. While there are increasing efforts to create adequate, open-source benchmark datasets for English, comparable resources are virtually unavailable for German, leaving research for the German language lagging significantly behind. In this paper, we introduce the new benchmark dataset FANG-COVID consisting of 28,056 real and 13,186 fake German news articles related to the COVID-19 pandemic as well as data on their propagation on Twitter. Furthermore, we propose an explainable textual- and social context-based model for fake news detection, compare its performance to “black-box” models and perform feature ablation to assess the relative importance of human-interpretable features in distinguishing fake news from authentic news.
DramaCoref: A Hybrid Coreference Resolution System for German Theater Plays

We present a system for resolving coreference on theater plays, DramaCoref. The system uses neural network techniques to provide a list of potential mentions. These mentions are assigned to common entities using generic and domain-specific rules. We find that DramaCoref works well on the theater plays when compared to corpora from other domains and profits from the inclusion of information specific to theater plays. On the best-performing setup, it achieves a CoNLL score of 32% when using automatically detected mentions and 55% when using gold mentions. Single rules achieve high precision scores; however, rules designed on other domains are often not applicable or yield unsatisfactory results. Error analysis shows that the mention detection is the main weakness of the system, providing directions for future improvements.
Knowledge and Keywords Augmented Abstractive Sentence Summarization

In this paper, we study the abstractive sentence summarization. There are two essential information features that can influence the quality of news summarization, which are topic keywords and the knowledge structure of the news text. Besides, the existing knowledge encoder has poor performance on sparse sentence knowledge structure. Considering these, we propose KAS, a novel Knowledge and Keywords Augmented Abstractive Sentence Summarization framework. Tri-encoders are utilized to integrate contexts of original text, knowledge structure and keywords topic simultaneously, with a special linearized knowledge structure. Automatic and human evaluations demonstrate that KAS achieves the best performances.
The Multilingual Corpus of Survey Questionnaires Query Interface

The dawn of the digital age led to increasing demands for digital research resources, which shall be quickly processed and handled by computers. Due to the amount of data created by this digitization process, the design of tools that enable the analysis and management of data and metadata has become a relevant topic. In this context, the Multilingual Corpus of Survey Questionnaires (MCSQ) contributes to the creation and distribution of data for the Social Sciences and Humanities (SSH) following FAIR (Findable, Accessible, Interoperable and Reusable) principles, and provides functionalities for end-users that are not acquainted with programming through an easy-to-use interface. By simply applying the desired filters in the graphic interface, users can build linguistic resources for the survey research and translation areas, such as translation memories, thus facilitating data access and usage.
Matching The Statements: A Simple and Accurate Model for Key Point Analysis

Key Point Analysis (KPA) is one of the most essential tasks in building an Opinion Summarization system, which is capable of generating key points for a collection of arguments toward a particular topic. Furthermore, KPA allows quantifying the coverage of each summary by counting its matched arguments. With the aim of creating high-quality summaries, it is necessary to have an in-depth understanding of each individual argument as well as its universal semantic in a specified context. In this paper, we introduce a promising model, named Matching the Statements (MTS) that incorporates the discussed topic information into arguments/key points comprehension to fully understand their meanings, thus accurately performing ranking and retrieving best-match key points for an input argument. Our approach has achieved the 4th place in Track 1 of the Quantitative Summarization – Key Point Analysis Shared Task by IBM, yielding a competitive performance of 0.8956 (3rd) and 0.9632 (7th) strict and relaxed mean Average Precision, respectively.
Can predicate-argument relationships be extracted from UD trees?

In this paper we investigate the possibility of extracting predicate-argument relations from UD trees (and enhanced UD graphs). Con- cretely, we apply UD parsers on an En- glish question answering/semantic-role label- ing data set (FitzGerald et al., 2018) and check if the annotations reflect the relations in the resulting parse trees, using a small number of rules to extract this information. We find that 79.1% of the argument-predicate pairs can be found in this way, on the basis of Ud- ify (Kondratyuk and Straka, 2019). Error anal- ysis reveals that half of the error cases are at- tributable to shortcomings in the dataset. The remaining errors are mostly due to predicate- argument relations not being extractible algo- rithmically from the UD trees (requiring se- mantic reasoning to be resolved). The parser itself is only responsible for a small portion of errors. Our analysis suggests a number of improvements to the UD annotation schema: we propose to enhance the schema in four ways, in order to capture argument-predicate relations. Additionally, we propose improve- ments regarding data collection for question answering/semantic-role labeling data.
M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and Transcripts

Argumentation mining aims at extracting, analysing and modelling people’s arguments, but large, high-quality annotated datasets are limited, and no multimodal datasets exist for this task. In this paper, we present M-Arg, a multimodal argument mining dataset with a corpus of US 2020 presidential debates, annotated through crowd-sourced annotations. This dataset allows models to be trained to extract arguments from natural dialogue such as debates using information like the intonation and rhythm of the speaker. Our dataset contains 7 hours of annotated US presidential debates, 6527 utterances and 4104 relation labels, and we report results from different baseline models, namely a text-only model, an audio-only model and multimodal models that extract features from both text and audio. With accuracy reaching 0.86 in multimodal models, we find that audio features provide added value with respect to text-only models.
Image Retrieval for Arguments Using Stance-Aware Query Expansion

Many forms of argumentation employ images as persuasive means, but research in argument mining has been focused on verbal argumentation so far. This paper shows how to integrate images into argument mining research, specifically into argument retrieval. By exploiting the sophisticated image representations of keyword-based image search, we propose to use semantic query expansion for both the pro and the con stance to retrieve “argumentative images” for the respective stance. Our results indicate that even simple expansions provide a strong baseline, reaching a precision@10 of 0.49 for images being (1) on-topic, (2) argumentative, and (3) on-stance. An in-depth analysis reveals a high topic dependence of the retrieval performance and shows the need to further investigate on images providing contextual information.
Do not neglect related languages: The case of low-resource Occitan cross-lingual word embeddings

Cross-lingual word embeddings (CLWEs) have proven indispensable for various natural language processing tasks, e.g., bilingual lexicon induction (BLI). However, the lack of data often impairs the quality of representations. Various approaches requiring only weak cross-lingual supervision were proposed, but current methods still fail to learn good CLWEs for languages with only a small monolingual corpus. We therefore claim that it is necessary to explore further datasets to improve CLWEs in low-resource setups. In this paper we propose to incorporate data of related high-resource languages. In contrast to previous approaches which leverage independently pre-trained embeddings of languages, we (i) train CLWEs for the low-resource and a related language jointly and (ii) map them to the target language to build the final multilingual space. In our experiments we focus on Occitan, a low-resource Romance language which is often neglected due to lack of resources. We leverage data from French, Spanish and Catalan for training and evaluate on the Occitan-English BLI task. By incorporating supporting languages our method outperforms previous approaches by a large margin. Furthermore, our analysis shows that the degree of relatedness between an incorporated language and the low-resource language is critically important.
A Fine-Grained Annotated Corpus for Target-Based Opinion Analysis of Economic and Financial Narratives

In this paper about aspect-based sentiment analysis (ABSA), we present the first version of a fine-grained annotated corpus for target-based opinion analysis (TBOA) to analyze economic activities or financial markets. We have annotated, at an intra-sentential level, a corpus of sentences extracted from documents representative of financial analysts’ most-read materials by considering how financial actors communicate about the evolution of event trends and analyze related publications (news, official communications, etc.). Since we focus on identifying the expressions of opinions related to the economy and financial markets, we annotated the sentences that contain at least one subjective expression about a domain-specific term. Candidate sentences for annotations were randomly chosen from texts of specialized press and professional information channels over a period ranging from 1986 to 2021. Our annotation scheme relies on various linguistic markers like domain-specific vocabulary, syntactic structures, and rhetorical relations to explicitly describe the author’s subjective stance. We investigated and evaluated the recourse to automatic pre-annotation with existing natural language processing technologies to alleviate the annotation workload. Our aim is to propose a corpus usable on the one hand as training material for the automatic detection of the opinions expressed on an extensive range of domain-specific aspects and on the other hand as a gold standard for evaluation TBOA. In this paper, we present our pre-annotation models and evaluations of their performance, introduce our annotation scheme and report on the main characteristics of our corpus.
Referenceless Parsing-Based Evaluation of AMR-to-English Generation

Reference-based automatic evaluation metrics are notoriously limited for NLG due to their inability to fully capture the range of possible outputs. We examine a referenceless alternative: evaluating the adequacy of English sentences generated from Abstract Meaning Representation (AMR) graphs by parsing into AMR and comparing the parse directly to the input. We ﬁnd that the errors introduced by automatic AMR parsing substantially limit the effectiveness of this approach, but a manual editing study indicates that as parsing improves, parsing-based evaluation has the potential to outperform most reference-based metrics.
Identifying the Importance of Content Overlap for Better Cross-lingual Embedding Mappings

In this work, we analyze the performance and properties of cross-lingual word embedding models created by mapping-based alignment methods. We use several measures of corpus and embedding similarity to predict BLI scores of cross-lingual embedding mappings over three types of corpora, three embedding methods and 55 language pairs. Our experimental results corroborate that instead of mere size, the amount of common content in the training corpora is essential. This phenomenon manifests in that i) despite of the smaller corpus sizes, using only the comparable parts of Wikipedia for training the monolingual embedding spaces to be mapped is often more efficient than relying on all the contents of Wikipedia, ii) the smaller, in return less diversified Spanish Wikipedia works almost always much better as a training corpus for bilingual mappings than the ubiquitously used English Wikipedia.
DreamDrug - A crowdsourced NER dataset for detecting drugs in darknet markets

We present DreamDrug, a crowdsourced dataset for detecting mentions of drugs in noisy user-generated item listings from darknet markets. Our dataset contains nearly 15,000 manually annotated drug entities in over 3,500 item listings scraped from the darknet market platform “DreamMarket” in 2017. We also train and evaluate baseline models for detecting these entities, using contextual language models fine-tuned in a few-shot setting and on the full dataset, and examine the effect of pretraining on in-domain unannotated corpora.