Angeliki Lazaridou


2020

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Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning
Angeliki Lazaridou | Anna Potapenko | Olivier Tieleman
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We present a method for combining multi-agent communication and traditional data-driven approaches to natural language learning, with an end goal of teaching agents to communicate with humans in natural language. Our starting point is a language model that has been trained on generic, not task-specific language data. We then place this model in a multi-agent self-play environment that generates task-specific rewards used to adapt or modulate the model, turning it into a task-conditional language model. We introduce a new way for combining the two types of learning based on the idea of reranking language model samples, and show that this method outperforms others in communicating with humans in a visual referential communication task. Finally, we present a taxonomy of different types of language drift that can occur alongside a set of measures to detect them.

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Experience Grounds Language
Yonatan Bisk | Ari Holtzman | Jesse Thomason | Jacob Andreas | Yoshua Bengio | Joyce Chai | Mirella Lapata | Angeliki Lazaridou | Jonathan May | Aleksandr Nisnevich | Nicolas Pinto | Joseph Turian
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

2017

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Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP
Samuel Bowman | Yoav Goldberg | Felix Hill | Angeliki Lazaridou | Omer Levy | Roi Reichart | Anders Søgaard
Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP

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The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
Nikita Nangia | Adina Williams | Angeliki Lazaridou | Samuel Bowman
Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP

This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017). All of the five participating teams beat the bidirectional LSTM (BiLSTM) and continuous bag of words baselines reported in Williams et al. The best single model used stacked BiLSTMs with residual connections to extract sentence features and reached 74.5% accuracy on the genre-matched test set. Surprisingly, the results of the competition were fairly consistent across the genre-matched and genre-mismatched test sets, and across subsets of the test data representing a variety of linguistic phenomena, suggesting that all of the submitted systems learned reasonably domain-independent representations for sentence meaning.

2016

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The LAMBADA dataset: Word prediction requiring a broad discourse context
Denis Paperno | Germán Kruszewski | Angeliki Lazaridou | Ngoc Quan Pham | Raffaella Bernardi | Sandro Pezzelle | Marco Baroni | Gemma Boleda | Raquel Fernández
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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The red one!: On learning to refer to things based on discriminative properties
Angeliki Lazaridou | Nghia The Pham | Marco Baroni
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Multimodal Learning and Reasoning
Desmond Elliott | Douwe Kiela | Angeliki Lazaridou
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

Natural Language Processing has broadened in scope to tackle more and more challenging language understanding and reasoning tasks. The core NLP tasks remain predominantly unimodal, focusing on linguistic input, despite the fact that we, humans, acquire and use language while communicating in perceptually rich environments. Moving towards human-level AI will require the integration and modeling of multiple modalities beyond language. With this tutorial, our aim is to introduce researchers to the areas of NLP that have dealt with multimodal signals. The key advantage of using multimodal signals in NLP tasks is the complementarity of the data in different modalities. For example, we are less likely to nd descriptions of yellow bananas or wooden chairs in text corpora, but these visual attributes can be readily extracted directly from images. Multimodal signals, such as visual, auditory or olfactory data, have proven useful for models of word similarity and relatedness, automatic image and video description, and even predicting the associated smells of words. Finally, multimodality offers a practical opportunity to study and apply multitask learning, a general machine learning paradigm that improves generalization performance of a task by using training signals of other related tasks.All material associated to the tutorial will be available at http://multimodalnlp.github.io/

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“Look, some Green Circles!”: Learning to Quantify from Images
Ionut Sorodoc | Angeliki Lazaridou | Gemma Boleda | Aurélie Herbelot | Sandro Pezzelle | Raffaella Bernardi
Proceedings of the 5th Workshop on Vision and Language

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Multimodal Semantic Learning from Child-Directed Input
Angeliki Lazaridou | Grzegorz Chrupała | Raquel Fernández | Marco Baroni
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Proceedings of the NAACL Student Research Workshop
Jacob Andreas | Eunsol Choi | Angeliki Lazaridou
Proceedings of the NAACL Student Research Workshop

2015

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Do Distributed Semantic Models Dream of Electric Sheep? Visualizing Word Representations through Image Synthesis
Angeliki Lazaridou | Dat Tien Nguyen | Marco Baroni
Proceedings of the Fourth Workshop on Vision and Language

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Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning
Angeliki Lazaridou | Georgiana Dinu | Marco Baroni
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model
Nghia The Pham | Germán Kruszewski | Angeliki Lazaridou | Marco Baroni
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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A Multitask Objective to Inject Lexical Contrast into Distributional Semantics
Nghia The Pham | Angeliki Lazaridou | Marco Baroni
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Combining Language and Vision with a Multimodal Skip-gram Model
Angeliki Lazaridou | Nghia The Pham | Marco Baroni
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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From Visual Attributes to Adjectives through Decompositional Distributional Semantics
Angeliki Lazaridou | Georgiana Dinu | Adam Liska | Marco Baroni
Transactions of the Association for Computational Linguistics, Volume 3

As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown…) attracting most attention. By building on the recent “zero-shot learning” approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available. Our approach relies on two key observations. First, objects can be seen as bundles of attributes, typically expressed as adjectival modifiers (a dog is something furry, brown, etc.), and thus a function trained to map visual representations of objects to nominal labels can implicitly learn to map attributes to adjectives. Second, objects and attributes come together in pictures (the same thing is a dog and it is brown). We can thus achieve better attribute (and object) label retrieval by treating images as “visual phrases”, and decomposing their linguistic representation into an attribute-denoting adjective and an object-denoting noun. Our approach performs comparably to a method exploiting manual attribute annotation, it out-performs various competitive alternatives in both attribute and object annotation, and it automatically constructs attribute-centric representations that significantly improve performance in supervised object recognition.

2014

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Coloring Objects: Adjective-Noun Visual Semantic Compositionality
Dat Tien Nguyen | Angeliki Lazaridou | Raffaella Bernardi
Proceedings of the Third Workshop on Vision and Language

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Cross-Language Authorship Attribution
Dasha Bogdanova | Angeliki Lazaridou
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents a novel task of cross-language authorship attribution (CLAA), an extension of authorship attribution task to multilingual settings: given data labelled with authors in language X, the objective is to determine the author of a document written in language Y , where X is different from Y . We propose a number of cross-language stylometric features for the task of CLAA, such as those based on sentiment and emotional markers. We also explore an approach based on machine translation (MT) with both lexical and cross-language features. We experimentally show that MT could be used as a starting point to CLAA, since it allows good attribution accuracy to be achieved. The cross-language features provide acceptable accuracy while using jointly with MT, though do not outperform lexical features.

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Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world
Angeliki Lazaridou | Elia Bruni | Marco Baroni
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Fish Transporters and Miracle Homes: How Compositional Distributional Semantics can Help NP Parsing
Angeliki Lazaridou | Eva Maria Vecchi | Marco Baroni
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Compositional-ly Derived Representations of Morphologically Complex Words in Distributional Semantics
Angeliki Lazaridou | Marco Marelli | Roberto Zamparelli | Marco Baroni
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations
Angeliki Lazaridou | Ivan Titov | Caroline Sporleder
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)