Daniel Loureiro


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TimeLMs: Diachronic Language Models from Twitter
Daniel Loureiro | Francesco Barbieri | Leonardo Neves | Luis Espinosa Anke | Jose Camacho-collados
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Despite its importance, the time variable has been largely neglected in the NLP and language model literature. In this paper, we present TimeLMs, a set of language models specialized on diachronic Twitter data. We show that a continual learning strategy contributes to enhancing Twitter-based language models’ capacity to deal with future and out-of-distribution tweets, while making them competitive with standardized and more monolithic benchmarks. We also perform a number of qualitative analyses showing how they cope with trends and peaks in activity involving specific named entities or concept drift. TimeLMs is available at github.com/cardiffnlp/timelms.

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Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)
Francesco Barbieri | Jose Camacho-Collados | Bhuwan Dhingra | Luis Espinosa-Anke | Elena Gribovskaya | Angeliki Lazaridou | Daniel Loureiro | Leonardo Neves
Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)

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TweetNLP: Cutting-Edge Natural Language Processing for Social Media
Jose Camacho-collados | Kiamehr Rezaee | Talayeh Riahi | Asahi Ushio | Daniel Loureiro | Dimosthenis Antypas | Joanne Boisson | Luis Espinosa Anke | Fangyu Liu | Eugenio Martínez Cámara
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. Task-specific systems are powered by reasonably-sized Transformer-based language models specialized on social media text (in particular, Twitter) which can be run without the need for dedicated hardware or cloud services. The main contributions of TweetNLP are: (1) an integrated Python library for a modern toolkit supporting social media analysis using our various task-specific models adapted to the social domain; (2) an interactive online demo for codeless experimentation using our models; and (3) a tutorial covering a wide variety of typical social media applications.

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TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media
Daniel Loureiro | Aminette D’Souza | Areej Nasser Muhajab | Isabella A. White | Gabriel Wong | Luis Espinosa-Anke | Leonardo Neves | Francesco Barbieri | Jose Camacho-Collados
Proceedings of the 29th International Conference on Computational Linguistics

Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.


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On the Cross-lingual Transferability of Contextualized Sense Embeddings
Kiamehr Rezaee | Daniel Loureiro | Jose Camacho-Collados | Mohammad Taher Pilehvar
Proceedings of the 1st Workshop on Multilingual Representation Learning

In this paper we analyze the extent to which contextualized sense embeddings, i.e., sense embeddings that are computed based on contextualized word embeddings, are transferable across languages. To this end, we compiled a unified cross-lingual benchmark for Word Sense Disambiguation. We then propose two simple strategies to transfer sense-specific knowledge across languages and test them on the benchmark. Experimental results show that this contextualized knowledge can be effectively transferred to similar languages through pre-trained multilingual language models, to the extent that they can out-perform monolingual representations learnednfrom existing language-specific data.

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Analysis and Evaluation of Language Models for Word Sense Disambiguation
Daniel Loureiro | Kiamehr Rezaee | Mohammad Taher Pilehvar | Jose Camacho-Collados
Computational Linguistics, Volume 47, Issue 2 - June 2021

Transformer-based language models have taken many fields in NLP by storm. BERT and its derivatives dominate most of the existing evaluation benchmarks, including those for Word Sense Disambiguation (WSD), thanks to their ability in capturing context-sensitive semantic nuances. However, there is still little knowledge about their capabilities and potential limitations in encoding and recovering word senses. In this article, we provide an in-depth quantitative and qualitative analysis of the celebrated BERT model with respect to lexical ambiguity. One of the main conclusions of our analysis is that BERT can accurately capture high-level sense distinctions, even when a limited number of examples is available for each word sense. Our analysis also reveals that in some cases language models come close to solving coarse-grained noun disambiguation under ideal conditions in terms of availability of training data and computing resources. However, this scenario rarely occurs in real-world settings and, hence, many practical challenges remain even in the coarse-grained setting. We also perform an in-depth comparison of the two main language model-based WSD strategies, namely, fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data. In fact, the simple feature extraction strategy of averaging contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements obtained by increasing the size of this training data.


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Don’t Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation
Daniel Loureiro | Jose Camacho-Collados
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

State-of-the-art methods for Word Sense Disambiguation (WSD) combine two different features: the power of pre-trained language models and a propagation method to extend the coverage of such models. This propagation is needed as current sense-annotated corpora lack coverage of many instances in the underlying sense inventory (usually WordNet). At the same time, unambiguous words make for a large portion of all words in WordNet, while being poorly covered in existing sense-annotated corpora. In this paper, we propose a simple method to provide annotations for most unambiguous words in a large corpus. We introduce the UWA (Unambiguous Word Annotations) dataset and show how a state-of-the-art propagation-based model can use it to extend the coverage and quality of its word sense embeddings by a significant margin, improving on its original results on WSD.


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LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)
Daniel Loureiro | Alípio Jorge
Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)

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Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation
Daniel Loureiro | Alípio Jorge
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we show that contextual embeddings can be used to achieve unprecedented gains in Word Sense Disambiguation (WSD) tasks. Our approach focuses on creating sense-level embeddings with full-coverage of WordNet, and without recourse to explicit knowledge of sense distributions or task-specific modelling. As a result, a simple Nearest Neighbors (k-NN) method using our representations is able to consistently surpass the performance of previous systems using powerful neural sequencing models. We also analyse the robustness of our approach when ignoring part-of-speech and lemma features, requiring disambiguation against the full sense inventory, and revealing shortcomings to be improved. Finally, we explore applications of our sense embeddings for concept-level analyses of contextual embeddings and their respective NLMs.


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Affordance Extraction and Inference based on Semantic Role Labeling
Daniel Loureiro | Alípio Jorge
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain which aspects of ‘coffee’ and ‘tea’ make them similar, or how they could be related to ‘shop’. In this paper, we propose an explicit word representation that builds upon the Distributional Hypothesis to represent meaning from semantic roles, and allow inference of relations from their meshing, as supported by the affordance-based Indexical Hypothesis. We find that our model improves the state-of-the-art on unsupervised word similarity tasks while allowing for direct inference of new relations from the same vector space.