Spain

Through comparison with several human-annotated reference sets, we find word2vec to be substantively superior to GloVe for unsupervised measurement
of the semantic compositionality of MWE candidates. We also 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.

COLING 2020

Comparing word2vec and GloVe for Automatic Measurement of MWE Compositionality

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

technical paper

In this presentation, we explore the categories of VMWEs that exist in Irish, including some of the challenges with annotating these categories. We present the resulting corpora and examine the results of the PARSEME Shared Task for Irish.

Annotating Verbal MWEs in Irish for the PARSEME Shared Task 1.2

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.

Embedding-Rich Multiword Expression Identification

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. A thorough comparison of both systems on eight languages reveals that despite the strong performance of multilingual contextual embeddings across all languages, language-specific contextual embeddings exhibit much better generalization capabilities.


How good is (m)BERT at seeing the unseen?


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.

Multi-word Expressions for Abusive Speech Detection in Serbian

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 on top of 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.


Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models

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.


Disambiguation of Potentially Idiomatic Expressions with Contextual Embeddings

In this presentation, I will provide an overview over the paper MultiVitaminBooster at PARSEME Shared Task 2020: Combining Window- and Dependency-Based Features with Multilingual Contextualized Word Embeddings for VMWE detection. In our approch to the shared task, we interpreted detecting verbal multiword expressions as a token classification task aiming to decide wether a given word is part of a VMWE. Our systems achieved subpar results which I discuss, as well.

MultiVitaminBooster at PARSEME Shared Task 2020

This paper describes a manually annotated corpus of verbal multi-word expressions in Polish. It is among the 4 biggest datasets in release 1.2 of the PARSEME multiligual corpus. We describe the data sources, as well as the annotation process and its outcomes. We also present interesting phenomena encountered during the annotation task and put forward enhancements for the
PARSEME annotation guidelines.

Polish corpus of verbal multiword expressions

The multiword adverbials N-BY-N and NUM-BY-NUM (as English brick by brick and one by one, respectively) are event modifiers which require temporal sequencing of the event they modify into a linearly ordered series of sub-events. Previous studies unified these two constructions under a single semantic analysis and adopted either a mereological or a scalar approach. However, based on a corpus study examining new Slavic language material and a binomial logistic regression modelling of the manually annotated data, we argue that two separate analyses are needed to account for these constructions, namely a scalar analysis for the N-BY-N construction and a mereological one for the NUM-BY-NUM construction.

Scalar vs. mereological conceptualizations of the N-BY-N and NUM-BY-NUM adverbials

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.

Comparing word2vec and GloVe for Automatic Measurement of MWE Compositionality

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Annotating Verbal MWEs in Irish for the PARSEME Shared Task 1.2

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Comparing word2vec and GloVe for Automatic Measurement of MWE Compositionality

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Annotating Verbal MWEs in Irish for the PARSEME Shared Task 1.2

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Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge

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