ymoslem/acl-abstracts huggingface dataset:
AlphaMWE: Construction of Multilingual Parallel Corpora with MWE Annotations (2020)

In this work, we present the construction of multilingual parallel corpora with annotation of multiword expressions (MWEs). MWEs include verbal MWEs (vMWEs) defined in the PARSEME shared task that have a verb as the head of the studied terms. The annotated vMWEs are also bilingually and multilingually aligned manually. The languages covered include English, Chinese, Polish, and German. Our original English corpus is taken from the PARSEME shared task in 2018. We performed machine translation of this source corpus followed by human post editing and annotation of target MWEs. Strict quality control was applied for error limitation, i.e., each MT output sentence received first manual post editing and annotation plus second manual quality rechecking. One of our findings during corpora preparation is that accurate translation of MWEs presents challenges to MT systems. To facilitate further MT research, we present a categorisation of the error types encountered by MT systems in performing MWE related translation. To acquire a broader view of MT issues, we selected four popular state-of-the-art MT models for comparisons namely: Microsoft Bing Translator, GoogleMT, Baidu Fanyi and DeepL MT. Because of the noise removal, translation post editing and MWE annotation by human professionals, we believe our AlphaMWE dataset will be an asset for cross-lingual and multilingual research, such as MT and information extraction. Our multilingual corpora are available as open access at github.com/poethan/AlphaMWE.
Annotating Verbal MWEs in Irish for the PARSEME Shared Task 1.2 (2020)

This paper describes the creation of two Irish corpora (labelled and unlabelled) for verbal MWEs for inclusion in the PARSEME Shared Task 1.2 on automatic identification of verbal MWEs, and the process of developing verbal MWE categories for Irish. A qualitative analysis on the two corpora is presented, along with discussion of Irish verbal MWEs.
VMWE discovery: a comparative analysis between Literature and Twitter Corpora (2020)

We evaluate manually five lexical association measurements as regards the discovery of Modern Greek verb multiword expressions with two or more lexicalised components usingmwetoolkit3 (Ramisch et al., 2010). We use Twitter corpora and compare our findings with previous work on fiction corpora. The results of LL, MLE and T-score were found to overlap significantly in both the fiction and the Twitter corpora, while the results of PMI and Dice do not. We find that MWEs with two lexicalised components are more frequent in Twitter than in fiction corpora and that lean syntactic patterns help retrieve them more efficiently than richer ones. Our work (i) supports the enrichment of the lexicographical database for Modern Greek MWEs' IDION' (Markantonatou et al., 2019) and (ii) highlights aspects of the usage of five association measurements on specific text genres for best MWE discovery results.
Invited Talk: Generationary or: \textquotedblleftHow We Went beyond Sense Inventories and Learned to Gloss\textquotedblright (2020)

In this talk I present Generationary, an approach that goes beyond the mainstream assumption that word senses can be represented as discrete items of a predefined inventory, and put forward a unified model which produces contextualized definitions for arbitrary lexical items, from words to phrases and even sentences. Generationary employs a novel span-based encoding scheme to fine-tune an English pre-trained Encoder-Decoder system and generate new definitions. Our model outperforms previous approaches in the generative task of Definition Modeling in many settings, but it also matches or surpasses the state of the art in discriminative tasks such as Word Sense Disambiguation and Word-in-Context. I also show that Generationary benefits from training on definitions from multiple inventories, with strong gains across benchmarks, including a novel dataset of definitions for free adjective-noun phrases, and discuss interesting examples of generated definitions. Joint work with Michele Bevilacqua and Marco Maru.
Multi-word Expressions for Abusive Speech Detection in Serbian (2020)

This paper presents our work on the refinement and improvement of the Serbian language part of Hurtlex, a multilingual lexicon of words to hurt. We pay special attention to adding Multi-word expressions that can be seen as abusive, as such lexical entries are very important in obtaining good results in a plethora of abusive language detection tasks. We use Serbian morphological dictionaries as a basis for data cleaning and MWE dictionary creation. A connection to other lexical and semantic resources in Serbian is outlined and building of abusive language detection systems based on that connection is foreseen.
Disambiguation of Potentially Idiomatic Expressions with Contextual Embeddings (2020)

The majority of multiword expressions can be interpreted as figuratively or literally in different contexts which pose challenges in a number of downstream tasks. Most previous work deals with this ambiguity following the observation that MWEs with different usages occur in distinctly different contexts. Following this insight, we explore the usefulness of contextual embeddings by means of both supervised and unsupervised classification. The results show that in the supervised setting, the state-of-the-art can be substantially improved for all expressions in the experiments. The unsupervised classification, similarly, yields very impressive results, comparing favorably to the supervised classifier for the majority of the expressions. We also show that multilingual contextual embeddings can also be employed for this task without leading to any significant loss in performance; hence, the proposed methodology has the potential to be extended to a number of languages.
Comparing word2vec and GloVe for Automatic Measurement of MWE Compositionality (2020)

This paper explores the use of word2vec and GloVe embeddings for unsupervised measurement of the semantic compositionality of MWE candidates. Through comparison with several human-annotated reference sets, we find word2vec to be substantively superior to GloVe for this task. We also find Simple English Wikipedia to be a poor-quality resource for compositionality assessment, but demonstrate that a sample of 10{\%} of sentences in the English Wikipedia can provide a conveniently tractable corpus with only moderate reduction in the quality of outputs.
Automatic detection of unexpected/erroneous collocations in learner corpus (2020)

This research investigates the collocational errors made by English learners in a learner corpus. It focuses on the extraction of unexpected collocations. A system was proposed and implemented with open source toolkit. Firstly, the collocation extraction module was evaluated by a corpus with manually annotated collocations. Secondly, a standard collocation list was collected from a corpus of native speaker. Thirdly, a list of unexpected collocations was generated by extracting candidates from a learner corpus and discarding the standard collocations on the list. The overall performance was evaluated, and possible sources of error were pointed out for future improvement.
Edition 1.2 of the PARSEME Shared Task on Semi-supervised Identification of Verbal Multiword Expressions (2020)

We present edition 1.2 of the PARSEME shared task on identification of verbal multiword expressions (VMWEs). Lessons learned from previous editions indicate that VMWEs have low ambiguity, and that the major challenge lies in identifying test instances never seen in the training data. Therefore, this edition focuses on unseen VMWEs. We have split annotated corpora so that the test corpora contain around 300 unseen VMWEs, and we provide non-annotated raw corpora to be used by complementary discovery methods. We released annotated and raw corpora in 14 languages, and this semi-supervised challenge attracted 7 teams who submitted 9 system results. This paper describes the effort of corpus creation, the task design, and the results obtained by the participating systems, especially their performance on unseen expressions.
HMSid and HMSid2 at PARSEME Shared Task 2020: Computational Corpus Linguistics and unseen-in-training MWEs (2020)

This paper is a system description of HMSid, officially sent to the PARSEME Shared Task 2020 for one language (French), in the open track. It also describes HMSid2, sent to the organ-izers of the workshop after the deadline and using the same methodology but in the closed track. Both systems do not rely on machine learning, but on computational corpus linguistics. Their score for unseen MWEs is very promising, especially in the case of HMSid2, which would have received the best score for unseen MWEs in the French closed track.
Seen2Unseen at PARSEME Shared Task 2020: All Roads do not Lead to Unseen Verb-Noun VMWEs (2020)

We describe the Seen2Unseen system that participated in edition 1.2 of the PARSEME shared task on automatic identification of verbal multiword expressions (VMWEs). The identification of VMWEs that do not appear in the provided training corpora (called unseen VMWEs) {--} with a focus here on verb-noun VMWEs {--} is based on mutual information and lexical substitution or translation of seen VMWEs. We present the architecture of the system, report results for 14 languages, and propose an error analysis.
ERMI at PARSEME Shared Task 2020: Embedding-Rich Multiword Expression Identification (2020)

This paper describes the ERMI system submitted to the closed track of the PARSEME shared task 2020 on automatic identification of verbal multiword expressions (VMWEs). ERMI is an embedding-rich bidirectional LSTM-CRF model, which takes into account the embeddings of the word, its POS tag, dependency relation, and its head word. The results are reported for 14 languages, where the system is ranked 1st in the general cross-lingual ranking of the closed track systems, according to the Unseen MWE-based F1.
TRAVIS at PARSEME Shared Task 2020: How good is (m)BERT at seeing the unseen? (2020)

This paper describes the TRAVIS system built for the PARSEME Shared Task 2020 on semi-supervised identification of verbal multiword expressions. TRAVIS is a fully feature-independent model, relying only on the contextual embeddings. We have participated with two variants of TRAVIS, TRAVIS-multi and TRAVIS-mono, where the former employs multilingual contextual embeddings and the latter uses monolingual ones. Our systems are ranked second and third among seven submissions in the open track, respectively. Despite the strong performance of multilingual contextual embeddings across all languages, the results show that language-specific contextual embeddings have better generalization capabilities.
MTLB-STRUCT @Parseme 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models (2020)

This paper describes a semi-supervised system that jointly learns verbal multiword expressions (VMWEs) and dependency parse trees as an auxiliary task. The model benefits from pre-trained multilingual BERT. BERT hidden layers are shared among the two tasks and we introduce an additional linear layer to retrieve VMWE tags. The dependency parse tree prediction is modelled by a linear layer and a bilinear one plus a tree CRF architecture on top of the shared BERT. The system has participated in the open track of the PARSEME shared task 2020 and ranked first in terms of F1-score in identifying unseen VMWEs as well as VMWEs in general, averaged across all 14 languages.
MultiVitaminBooster at PARSEME Shared Task 2020: Combining Window- and Dependency-Based Features with Multilingual Contextualised Word Embeddings for VMWE Detection (2020)

In this paper, we present MultiVitaminBooster, a system implemented for the PARSEME shared task on semi-supervised identification of verbal multiword expressions - edition 1.2. For our approach, we interpret detecting verbal multiword expressions as a token classification task aiming to decide whether a token is part of a verbal multiword expression or not. For this purpose, we train gradient boosting-based models. We encode tokens as feature vectors combining multilingual contextualized word embeddings provided by the XLM-RoBERTa language model with a more traditional linguistic feature set relying on context windows and dependency relations. Our system was ranked 7th in the official open track ranking of the shared task evaluations with an encoding-related bug distorting the results. For this reason we carry out further unofficial evaluations. Unofficial versions of our systems would have achieved higher ranks.
Detecting Adverse Drug Events from Swedish Electronic Health Records using Text Mining (2020)

Electronic Health Records are a valuable source of patient information which can be leveraged to detect Adverse Drug Events (ADEs) and aid post-mark drug-surveillance. The overall aim of this study is to scrutinize text written by clinicians in the EHRs and build a model for ADE detection that produces medically relevant predictions. Natural Language Processing techniques will be exploited to create important predictors and incorporate them into the learning process. The study focuses on the 5 most frequent ADE cases found ina Swedish electronic patient record corpus. The results indicate that considering textual features, rather than the structured, can improve the classification performance by 15{\%} in some ADE cases. Additionally, variable patient history lengths are incorporated in the models, demonstrating the importance of the above decision rather than using an arbitrary number for a history length. The experimental findings suggest that the clinical text in EHRs includes information that can capture data beyond the ones that are found in a structured format.
Building a Norwegian Lexical Resource for Medical Entity Recognition (2020)

We present a large Norwegian lexical resource of categorized medical terms. The resource, which merges information from large medical databases, contains over 56,000 entries, including automatically mapped terms from a Norwegian medical dictionary. We describe the methodology behind this automatic dictionary entry mapping based on keywords and suffixes and further present the results of a manual evaluation performed on a subset by a domain expert. The evaluation indicated that ca. 80{\%} of the mappings were correct.
Localising the Clinical Terminology SNOMED CT by Semi-automated Creation of a German Interface Vocabulary (2020)

Medical language exhibits great variations regarding users, institutions and language registers. With large parts of clinical documents in free text, NLP is playing a more and more important role in unlocking re-usable and interoperable meaning from medical records. This study describes the architectural principles and the evolution of a German interface vocabulary, combining machine translation with human annotation and rule-based term generation, yielding a resource with 7.7 million raw entries, each of which linked to the reference terminology SNOMED CT, an international standard with about 350 thousand concepts. The purpose is to offer a high coverage of medical jargon in order to optimise terminology grounding of clinical texts by text mining systems. The core resource is a manually curated table of English-to-German word and chunk translations, supported by a set of language generation rules. The work describes a workflow consisting the enrichment and modification of this table with human and machine efforts, manually enriched by grammarspecific tags. Top-down and bottom-up methods for terminology population used in parallel. The final interface terms are produced by a term generator, which creates one-to-many German variants per SNOMED CT English description. Filtering against a large collection of domain terminologies and corpora drastically reduces the size of the vocabulary in favour of more realistic terms or terms that can reasonably be expected to match clinical text passages within a text-mining pipeline. An evaluation was performed by a comparison between the current version of the German interface vocabulary and the English description table of the SNOMED CT International release. An exact term matching was performed with a small parallel corpus constituted by text snippets from different clinical documents. With overall low retrieval parameters (with F-values around 30{\%}), the performance of the German language scenario reaches 80 {--} 90{\%} of the English one. Interestingly, annotations are slightly better with machine-translated (German {--} English) texts, using the International SNOMED CT resource only.
Multilingual enrichment of disease biomedical ontologies (2020)

Translating biomedical ontologies is an important challenge, but doing it manually requires much time and money. We study the possibility to use open-source knowledge bases to translate biomedical ontologies. We focus on two aspects: coverage and quality. We look at the coverage of two biomedical ontologies focusing on diseases with respect to Wikidata for 9 European languages (Czech, Dutch, English, French, German, Italian, Polish, Portuguese and Spanish) for both, plus Arabic, Chinese and Russian for the second. We first use direct links between Wikidata and the studied ontologies and then use second-order links by going through other intermediate ontologies. We then compare the quality of the translations obtained thanks to Wikidata with a commercial machine translation tool, here Google Cloud Translation.
Transfer learning applied to text classification in Spanish radiological reports (2020)

Pre-trained text encoders have rapidly advanced the state-of-the-art on many Natural Language Processing tasks. This paper presents the use of transfer learning methods applied to the automatic detection of codes in radiological reports in Spanish. Assigning codes to a clinical document is a popular task in NLP and in the biomedical domain. These codes can be of two types: standard classifications (e.g. ICD-10) or specific to each clinic or hospital. In this study we show a system using specific radiology clinic codes. The dataset is composed of 208,167 radiology reports labeled with 89 different codes. The corpus has been evaluated with three methods using the BERT model applied to Spanish: Multilingual BERT, BETO and XLM. The results are interesting obtaining 70{\%} of F1-score with a pre-trained multilingual model.
Automated Processing of Multilingual Online News for the Monitoring of Animal Infectious Diseases (2020)

The Platform for Automated extraction of animal Disease Information from the web (PADI-web) is an automated system which monitors the web for monitoring and detecting emerging animal infectious diseases. The tool automatically collects news via customised multilingual queries, classifies them and extracts epidemiological information. We detail the processing of multilingual online sources by PADI-web and analyse the translated outputs in a case study
The Third Multilingual Surface Realisation Shared Task (SR`20): Overview and Evaluation Results (2020)

This paper presents results from the Third Shared Task on Multilingual Surface Realisation (SR`20) which was organised as part of the COLING`20 Workshop on Multilingual Surface Realisation. As in SR`18 and SR`19, the shared task comprised two tracks: (1) a Shallow Track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (2) a Deep Track where additionally, functional words and morphological information were removed. Moreover, each track had two subtracks: (a) restricted-resource, where only the data provided or approved as part of a track could be used for training models, and (b) open-resource, where any data could be used. The Shallow Track was offered in 11 languages, whereas the Deep Track in 3 ones. Systems were evaluated using both automatic metrics and direct assessment by human evaluators in terms of Readability and Meaning Similarity to reference outputs. We present the evaluation results, along with descriptions of the SR`19 tracks, data and evaluation methods, as well as brief summaries of the participating systems. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.
BME-TUW at SR`20: Lexical grammar induction for surface realization (2020)

We present a system for mapping Universal Dependency structures to raw text which learns to restore word order by training an Interpreted Regular Tree Grammar (IRTG) that establishes a mapping between string and graph operations. The reinflection step is handled by a standard sequence-to-sequence architecture with a biLSTM encoder and an LSTM decoder with attention. We modify our 2019 system (Kov{\'a}cs et al., 2019) with a new grammar induction mechanism that allows IRTG rules to operate on lemmata in addition to part-of-speech tags and ensures that each word and its dependents are reordered using the most specific set of learned patterns. We also introduce a hierarchical approach to word order restoration that independently determines the word order of each clause in a sentence before arranging them with respect to the main clause, thereby improving overall readability and also making the IRTG parsing task tractable. We participated in the 2020 Surface Realization Shared task, subtrack T1a (shallow, closed). Human evaluation shows we achieve significant improvements on two of the three out-of-domain datasets compared to the 2019 system we modified. Both components of our system are available on GitHub under an MIT license.
ADAPT at SR`20: How Preprocessing and Data Augmentation Help to Improve Surface Realization (2020)

In this paper, we describe the ADAPT submission to the Surface Realization Shared Task 2020. We present a neural-based system trained on the English Web Treebank and an augmented dataset, automatically created from existing text corpora.
IMSurReal Too: IMS in the Surface Realization Shared Task 2020 (2020)

We introduce the IMS contribution to the Surface Realization Shared Task 2020. The new system achieves substantial improvement over the state-of-the-art system from last year, mainly due to a better token representation and a better linearizer, as well as a simple ensembling approach. We also experiment with data augmentation, which brings some additional performance gain. The system is available at \url{https://github.com/EggplantElf/IMSurReal}.
Lexical Induction of Morphological and Orthographic Forms for Low-Resourced Languages (2020)

In this work we address the issue of high-degree lexical sparsity for non-standard languages under severe circumstance of small resources that are considered insufficient to train recent powerful language models. We proposed a new rule-based approach and utilised word embeddings to connect words with their inflectional and orthographic forms from a given corpus. Our case example is the low-resourced Lebanese dialect Arabizi. Arabizi is the name given to a new social transcription of the spoken Arabic in Latin script. The term comes from the portmanteau of Araby (Arabic) and Englizi (English). It is an informal written language where Arabs transcribe their dialectal mother tongue in text using Latin alphanumeral instead of Arabic script. For example حبيبي Ḥab{\={i}}b{\={i}} my-love could be transcribed as 7abibi in Arabizi. We induced 175K forms from a list of 1.7K sentiment words. We evaluated this induction extrinsically on a sentiment-annotated dataset pushing its coverage by 13{\%} over the previous version. We named the new lexicon SenZi-Large and released it publicly.