Aina Garí Soler


2023

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Un mot, deux facettes : traces des opinions dans les représentations contextualisées des mots
Aina Garí Soler | Matthieu Labeau | Chloe Clavel
Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 4 : articles déjà soumis ou acceptés en conférence internationale

La façon dont nous utilisons les mots est influencée par notre opinion. Nous cherchons à savoir si cela se reflète dans les plongements de mots contextualisés. Par exemple, la représentation d’ « animal » est-elle différente pour les gens qui voudraient abolir les zoos et ceux qui ne le voudraient pas ? Nous explorons cette question du point de vue du changement sémantique des mots. Nos expériences avec des représentations dérivées d’ensembles de données annotés avec les points de vue révèlent des différences minimes, mais significatives, entre postures opposées.

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Measuring Lexico-Semantic Alignment in Debates with Contextualized Word Representations
Aina Garí Soler | Matthieu Labeau | Chloé Clavel
Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)

Dialog participants sometimes align their linguistic styles, e.g., they use the same words and syntactic constructions as their interlocutors. We propose to investigate the notion of lexico-semantic alignment: to what extent do speakers convey the same meaning when they use the same words? We design measures of lexico-semantic alignment relying on contextualized word representations. We show that they reflect interesting semantic differences between the two sides of a debate and that they can assist in the task of debate’s winner prediction.

2022

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One Word, Two Sides: Traces of Stance in Contextualized Word Representations
Aina Garí Soler | Matthieu Labeau | Chloé Clavel
Proceedings of the 29th International Conference on Computational Linguistics

The way we use words is influenced by our opinion. We investigate whether this is reflected in contextualized word embeddings. For example, is the representation of “animal” different between people who would abolish zoos and those who would not? We explore this question from a Lexical Semantic Change standpoint. Our experiments with BERT embeddings derived from datasets with stance annotations reveal small but significant differences in word representations between opposing stances.

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Polysemy in Spoken Conversations and Written Texts
Aina Garí Soler | Matthieu Labeau | Chloé Clavel
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Our discourses are full of potential lexical ambiguities, due in part to the pervasive use of words having multiple senses. Sometimes, one word may even be used in more than one sense throughout a text. But, to what extent is this true for different kinds of texts? Does the use of polysemous words change when a discourse involves two people, or when speakers have time to plan what to say? We investigate these questions by comparing the polysemy level of texts of different nature, with a focus on spontaneous spoken dialogs; unlike previous work which examines solely scripted, written, monolog-like data. We compare multiple metrics that presuppose different conceptualizations of text polysemy, i.e., they consider the observed or the potential number of senses of words, or their sense distribution in a discourse. We show that the polysemy level of texts varies greatly depending on the kind of text considered, with dialog and spoken discourses having generally a higher polysemy level than written monologs. Additionally, our results emphasize the need for relaxing the popular “one sense per discourse” hypothesis.

2021

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ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns’ Semantic Properties and their Prototypicality
Marianna Apidianaki | Aina Garí Soler
Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Large scale language models encode rich commonsense knowledge acquired through exposure to massive data during pre-training, but their understanding of entities and their semantic properties is unclear. We probe BERT (Devlin et al., 2019) for the properties of English nouns as expressed by adjectives that do not restrict the reference scope of the noun they modify (as in “red car”), but instead emphasise some inherent aspect (“red strawberry”). We base our study on psycholinguistics datasets that capture the association strength between nouns and their semantic features. We probe BERT using cloze tasks and in a classification setting, and show that the model has marginal knowledge of these features and their prevalence as expressed in these datasets. We discuss factors that make evaluation challenging and impede drawing general conclusions about the models’ knowledge of noun properties. Finally, we show that when tested in a fine-tuning setting addressing entailment, BERT successfully leverages the information needed for reasoning about the meaning of adjective-noun constructions outperforming previous methods.

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Scalar Adjective Identification and Multilingual Ranking
Aina Garí Soler | Marianna Apidianaki
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The intensity relationship that holds between scalar adjectives (e.g., nice < great < wonderful) is highly relevant for natural language inference and common-sense reasoning. Previous research on scalar adjective ranking has focused on English, mainly due to the availability of datasets for evaluation. We introduce a new multilingual dataset in order to promote research on scalar adjectives in new languages. We perform a series of experiments and set performance baselines on this dataset, using monolingual and multilingual contextual language models. Additionally, we introduce a new binary classification task for English scalar adjective identification which examines the models’ ability to distinguish scalar from relational adjectives. We probe contextualised representations and report baseline results for future comparison on this task.

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Let’s Play Mono-Poly: BERT Can Reveal Words’ Polysemy Level and Partitionability into Senses
Aina Garí Soler | Marianna Apidianaki
Transactions of the Association for Computational Linguistics, Volume 9

Pre-trained language models (LMs) encode rich information about linguistic structure but their knowledge about lexical polysemy remains unclear. We propose a novel experimental setup for analyzing this knowledge in LMs specifically trained for different languages (English, French, Spanish, and Greek) and in multilingual BERT. We perform our analysis on datasets carefully designed to reflect different sense distributions, and control for parameters that are highly correlated with polysemy such as frequency and grammatical category. We demonstrate that BERT-derived representations reflect words’ polysemy level and their partitionability into senses. Polysemy-related information is more clearly present in English BERT embeddings, but models in other languages also manage to establish relevant distinctions between words at different polysemy levels. Our results contribute to a better understanding of the knowledge encoded in contextualized representations and open up new avenues for multilingual lexical semantics research.

2020

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MULTISEM at SemEval-2020 Task 3: Fine-tuning BERT for Lexical Meaning
Aina Garí Soler | Marianna Apidianaki
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We present the MULTISEM systems submitted to SemEval 2020 Task 3: Graded Word Similarity in Context (GWSC). We experiment with injecting semantic knowledge into pre-trained BERT models through fine-tuning on lexical semantic tasks related to GWSC. We use existing semantically annotated datasets, and propose to approximate similarity through automatically generated lexical substitutes in context. We participate in both GWSC subtasks and address two languages, English and Finnish. Our best English models occupy the third and fourth positions in the ranking for the two subtasks. Performance is lower for the Finnish models which are mid-ranked in the respective subtasks, highlighting the important role of data availability for fine-tuning.

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BERT Knows Punta Cana is not just beautiful, it’s gorgeous: Ranking Scalar Adjectives with Contextualised Representations
Aina Garí Soler | Marianna Apidianaki
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Adjectives like pretty, beautiful and gorgeous describe positive properties of the nouns they modify but with different intensity. These differences are important for natural language understanding and reasoning. We propose a novel BERT-based approach to intensity detection for scalar adjectives. We model intensity by vectors directly derived from contextualised representations and show they can successfully rank scalar adjectives. We evaluate our models both intrinsically, on gold standard datasets, and on an Indirect Question Answering task. Our results demonstrate that BERT encodes rich knowledge about the semantics of scalar adjectives, and is able to provide better quality intensity rankings than static embeddings and previous models with access to dedicated resources.

2019

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A Comparison of Context-sensitive Models for Lexical Substitution
Aina Garí Soler | Anne Cocos | Marianna Apidianaki | Chris Callison-Burch
Proceedings of the 13th International Conference on Computational Semantics - Long Papers

Word embedding representations provide good estimates of word meaning and give state-of-the art performance in semantic tasks. Embedding approaches differ as to whether and how they account for the context surrounding a word. We present a comparison of different word and context representations on the task of proposing substitutes for a target word in context (lexical substitution). We also experiment with tuning contextualized word embeddings on a dataset of sense-specific instances for each target word. We show that powerful contextualized word representations, which give high performance in several semantics-related tasks, deal less well with the subtle in-context similarity relationships needed for substitution. This is better handled by models trained with this objective in mind, where the inter-dependence between word and context representations is explicitly modeled during training.

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LIMSI-MULTISEM at the IJCAI SemDeep-5 WiC Challenge: Context Representations for Word Usage Similarity Estimation
Aina Garí Soler | Marianna Apidianaki | Alexandre Allauzen
Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)

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Exploring sentence informativeness
Syrielle Montariol | Aina Garí Soler | Alexandre Allauzen
Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts

This study is a preliminary exploration of the concept of informativeness –how much information a sentence gives about a word it contains– and its potential benefits to building quality word representations from scarce data. We propose several sentence-level classifiers to predict informativeness, and we perform a manual annotation on a set of sentences. We conclude that these two measures correspond to different notions of informativeness. However, our experiments show that using the classifiers’ predictions to train word embeddings has an impact on embedding quality.

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Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes
Aina Garí Soler | Marianna Apidianaki | Alexandre Allauzen
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Usage similarity estimation addresses the semantic proximity of word instances in different contexts. We apply contextualized (ELMo and BERT) word and sentence embeddings to this task, and propose supervised models that leverage these representations for prediction. Our models are further assisted by lexical substitute annotations automatically assigned to word instances by context2vec, a neural model that relies on a bidirectional LSTM. We perform an extensive comparison of existing word and sentence representations on benchmark datasets addressing both graded and binary similarity. The best performing models outperform previous methods in both settings.

2018

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A comparative study of word embeddings and other features for lexical complexity detection in French
Aina Garí Soler | Marianna Apidianaki | Alexandre Allauzen
Actes de la Conférence TALN. Volume 1 - Articles longs, articles courts de TALN

Lexical complexity detection is an important step for automatic text simplification which serves to make informed lexical substitutions. In this study, we experiment with word embeddings for measuring the complexity of French words and combine them with other features that have been shown to be well-suited for complexity prediction. Our results on a synonym ranking task show that embeddings perform better than other features in isolation, but do not outperform frequency-based systems in this language.