Douwe Kiela


2021

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On the Efficacy of Adversarial Data Collection for Question Answering : Results from a Large-Scale Randomized Study
Divyansh Kaushik | Douwe Kiela | Zachary C. Lipton | Wen-tau Yih
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that models trained on these more challenging datasets will rely less on superficial patterns, and thus be less brittle. However, despite ADC’s intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models. In this paper, we conduct a large-scale controlled study focused on question answering, assigning workers at random to compose questions either (i) adversarially (with a model in the loop) ; or (ii) in the standard fashion (without a model). Across a variety of models and datasets, we find that models trained on adversarial data usually perform better on other adversarial datasets but worse on a diverse collection of out-of-domain evaluation sets. Finally, we provide a qualitative analysis of adversarial (vs standard) data, identifying key differences and offering guidance for future research.

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Masked Language Modeling and the Distributional Hypothesis : Order Word Matters Pre-training for Little
Koustuv Sinha | Robin Jia | Dieuwke Hupkes | Joelle Pineau | Adina Williams | Douwe Kiela
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines. In this paper, we propose a different explanation : MLMs succeed on downstream tasks almost entirely due to their ability to model higher-order word co-occurrence statistics. To demonstrate this, we pre-train MLMs on sentences with randomly shuffled word order, and show that these models still achieve high accuracy after fine-tuning on many downstream tasksincluding tasks specifically designed to be challenging for models that ignore word order. Our models perform surprisingly well according to some parametric syntactic probes, indicating possible deficiencies in how we test representations for syntactic information. Overall, our results show that purely distributional information largely explains the success of pre-training, and underscore the importance of curating challenging evaluation datasets that require deeper linguistic knowledge.

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Gradient-based Adversarial Attacks against Text Transformers
Chuan Guo | Alexandre Sablayrolles | Hervé Jégou | Douwe Kiela
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We propose the first general-purpose gradient-based adversarial attack against transformer models. Instead of searching for a single adversarial example, we search for a distribution of adversarial examples parameterized by a continuous-valued matrix, hence enabling gradient-based optimization. We empirically demonstrate that our white-box attack attains state-of-the-art attack performance on a variety of natural language tasks, outperforming prior work in terms of adversarial success rate with matching imperceptibility as per automated and human evaluation. Furthermore, we show that a powerful black-box transfer attack, enabled by sampling from the adversarial distribution, matches or exceeds existing methods, while only requiring hard-label outputs.

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Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation
Max Bartolo | Tristan Thrush | Robin Jia | Sebastian Riedel | Pontus Stenetorp | Douwe Kiela
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Despite recent progress, state-of-the-art question answering models remain vulnerable to a variety of adversarial attacks. While dynamic adversarial data collection, in which a human annotator tries to write examples that fool a model-in-the-loop, can improve model robustness, this process is expensive which limits the scale of the collected data. In this work, we are the first to use synthetic adversarial data generation to make question answering models more robust to human adversaries. We develop a data generation pipeline that selects source passages, identifies candidate answers, generates questions, then finally filters or re-labels them to improve quality. Using this approach, we amplify a smaller human-written adversarial dataset to a much larger set of synthetic question-answer pairs. By incorporating our synthetic data, we improve the state-of-the-art on the AdversarialQA dataset by 3.7F1 and improve model generalisation on nine of the twelve MRQA datasets. We further conduct a novel human-in-the-loop evaluation and show that our models are considerably more robust to new human-written adversarial examples : crowdworkers can fool our model only 8.8 % of the time on average, compared to 17.6 % for a model trained without synthetic data.

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Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)
Aida Mostafazadeh Davani | Douwe Kiela | Mathias Lambert | Bertie Vidgen | Vinodkumar Prabhakaran | Zeerak Waseem
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

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Dynabench : Rethinking Benchmarking in NLPNLP
Douwe Kiela | Max Bartolo | Yixin Nie | Divyansh Kaushik | Atticus Geiger | Zhengxuan Wu | Bertie Vidgen | Grusha Prasad | Amanpreet Singh | Pratik Ringshia | Zhiyi Ma | Tristan Thrush | Sebastian Riedel | Zeerak Waseem | Pontus Stenetorp | Robin Jia | Mohit Bansal | Christopher Potts | Adina Williams
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop dataset creation : annotators seek to create examples that a target model will misclassify, but that another person will not. In this paper, we argue that Dynabench addresses a critical need in our community : contemporary models quickly achieve outstanding performance on benchmark tasks but nonetheless fail on simple challenge examples and falter in real-world scenarios. With Dynabench, dataset creation, model development, and model assessment can directly inform each other, leading to more robust and informative benchmarks. We report on four initial NLP tasks, illustrating these concepts and highlighting the promise of the platform, and address potential objections to dynamic benchmarking as a new standard for the field.

2020

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Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations
Derek Wong | Douwe Kiela
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations

2019

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Learning to Speak and Act in a Fantasy Text Adventure Game
Jack Urbanek | Angela Fan | Siddharth Karamcheti | Saachi Jain | Samuel Humeau | Emily Dinan | Tim Rocktäschel | Douwe Kiela | Arthur Szlam | Jason Weston
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We introduce a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game. We describe the results of training state-of-the-art generative and retrieval models in this setting. We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions. In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue. We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.

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Seeded self-play for language learning
Abhinav Gupta | Ryan Lowe | Jakob Foerster | Douwe Kiela | Joelle Pineau
Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)

How can we teach artificial agents to use human language flexibly to solve problems in real-world environments? We have an example of this in nature : human babies eventually learn to use human language to solve problems, and they are taught with an adult human-in-the-loop. Unfortunately, current machine learning methods (e.g. from deep reinforcement learning) are too data inefficient to learn language in this way. An outstanding goal is finding an algorithm with a suitable ‘language learning prior’ that allows it to learn human language, while minimizing the number of on-policy human interactions. In this paper, we propose to learn such a prior in simulation using an approach we call, Learning to Learn to Communicate (L2C). Specifically, in L2C we train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol. Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations, including populations speaking human language. Our key insight is that such populations can be obtained via self-play, after pre-training agents with imitation learning on a small amount of off-policy human language data. We call this latter technique Seeded Self-Play (S2P). Our preliminary experiments show that agents trained with L2C and S2P need fewer on-policy samples to learn a compositional language in a Lewis signaling game.

2018

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Dynamic Meta-Embeddings for Improved Sentence Representations
Douwe Kiela | Changhan Wang | Kyunghyun Cho
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves. To that end, we introduce dynamic meta-embeddings, a simple yet effective method for the supervised learning of embedding ensembles, which leads to state-of-the-art performance within the same model class on a variety of tasks. We subsequently show how the technique can be used to shed new light on the usage of word embeddings in NLP systems.

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Code-Switched Named Entity Recognition with Embedding Attention
Changhan Wang | Kyunghyun Cho | Douwe Kiela
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching

We describe our work for the CALCS 2018 shared task on named entity recognition on code-switched data. Our system ranked first place for MS Arabic-Egyptian named entity recognition and third place for English-Spanish.

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Personalizing Dialogue Agents : I have a dog, do you have pets too?I have a dog, do you have pets too?
Saizheng Zhang | Emily Dinan | Jack Urbanek | Arthur Szlam | Douwe Kiela | Jason Weston
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Chit-chat models are known to have several problems : they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i)condition on their given profile information ; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.

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Hearst Patterns Revisited : Automatic Hypernym Detection from Large Text Corpora
Stephen Roller | Douwe Kiela | Maximilian Nickel
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms : pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets. Our results show that pattern-based models provide important contextual constraints which are not yet captured in distributional methods.

2017

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Automatically Generating Rhythmic Verse with Neural Networks
Jack Hopkins | Douwe Kiela
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose two novel methodologies for the automatic generation of rhythmic poetry in a variety of forms. The first approach uses a neural language model trained on a phonetic encoding to learn an implicit representation of both the form and content of English poetry. This model can effectively learn common poetic devices such as rhyme, rhythm and alliteration. The second approach considers poetry generation as a constraint satisfaction problem where a generative neural language model is tasked with learning a representation of content, and a discriminative weighted finite state machine constrains it on the basis of form. By manipulating the constraints of the latter model, we can generate coherent poetry with arbitrary forms and themes. A large-scale extrinsic evaluation demonstrated that participants consider machine-generated poems to be written by humans 54 % of the time. In addition, participants rated a machine-generated poem to be the best amongst all evaluated.

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Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns
Andrew J. Anderson | Douwe Kiela | Stephen Clark | Massimo Poesio
Transactions of the Association for Computational Linguistics, Volume 5

Important advances have recently been made using computational semantic models to decode brain activity patterns associated with concepts ; however, this work has almost exclusively focused on concrete nouns. How well these models extend to decoding abstract nouns is largely unknown. We address this question by applying state-of-the-art computational models to decode functional Magnetic Resonance Imaging (fMRI) activity patterns, elicited by participants reading and imagining a diverse set of both concrete and abstract nouns. One of the models we use is linguistic, exploiting the recent word2vec skipgram approach trained on Wikipedia. The second is visually grounded, using deep convolutional neural networks trained on Google Images. Dual coding theory considers concrete concepts to be encoded in the brain both linguistically and visually, and abstract concepts only linguistically. Splitting the fMRI data according to human concreteness ratings, we indeed observe that both models significantly decode the most concrete nouns ; however, accuracy is significantly greater using the text-based models for the most abstract nouns. More generally this confirms that current computational models are sufficiently advanced to assist in investigating the representational structure of abstract concepts in the brain.

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HyperLex : A Large-Scale Evaluation of Graded Lexical EntailmentHyperLex: A Large-Scale Evaluation of Graded Lexical Entailment
Ivan Vulić | Daniela Gerz | Douwe Kiela | Felix Hill | Anna Korhonen
Computational Linguistics, Volume 43, Issue 4 - December 2017

We introduce HyperLexa data set and evaluation resource that quantifies the extent of the semantic category membership, that is, type-of relation, also known as hyponymyhypernymy or lexical entailment (LE) relation between 2,616 concept pairs. Cognitive psychology research has established that typicality and category / class membership are computed in human semantic memory as a gradual rather than binary relation. Nevertheless, most NLP research and existing large-scale inventories of concept category membership (WordNet, DBPedia, etc.) treat category membership and LE as binary. To address this, we asked hundreds of native English speakers to indicate typicality and strength of category membership between a diverse range of concept pairs on a crowdsourcing platform. Our results confirm that category membership and LE are indeed more gradual than binary. We then compare these human judgments with the predictions of automatic systems, which reveals a huge gap between human performance and state-of-the-art LE, distributional and representation learning models, and substantial differences between the models themselves. We discuss a pathway for improving semantic models to overcome this discrepancy, and indicate future application areas for improved graded LE systems.

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Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Alexis Conneau | Douwe Kiela | Holger Schwenk | Loïc Barrault | Antoine Bordes
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available.

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Grasping the Finer Point : A Supervised Similarity Network for Metaphor Detection
Marek Rei | Luana Bulat | Douwe Kiela | Ekaterina Shutova
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding. Yet, the majority of metaphor processing systems to date rely on hand-engineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.

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Learning to Negate Adjectives with Bilinear Models
Laura Rimell | Amandla Mabona | Luana Bulat | Douwe Kiela
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We learn a mapping that negates adjectives by predicting an adjective’s antonym in an arbitrary word embedding model. We show that both linear models and neural networks improve on this task when they have access to a vector representing the semantic domain of the input word, e.g. a centroid of temperature words when predicting the antonym of ‘cold’. We introduce a continuous class-conditional bilinear neural network which is able to negate adjectives with high precision.