Sebastian Riedel


2022

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Open Vocabulary Extreme Classification Using Generative Models
Daniel Simig | Fabio Petroni | Pouya Yanki | Kashyap Popat | Christina Du | Sebastian Riedel | Majid Yazdani
Findings of the Association for Computational Linguistics: ACL 2022

The extreme multi label classification XMC task aims at tagging content with a subset of labels from an extremely large label set The label vocabulary is typically defined in advance by domain experts and assumed to capture all necessary tags However in real world scenarios this label set although large is often incomplete and experts frequently need to refine it To develop systems that simplify this process we introduce the task of open vocabulary XMC OXMC): given a piece of content predict a set of labels some of which may be outside of the known tag set Hence in addition to not having training data for some labelsas is the case in zero shot classificationmodels need to invent some labels on thefly We propose GROOV a fine tuned seq2seq model for OXMC that generates the set of labels as a flat sequence and is trained using a novel loss independent of predicted label order We show the efficacy of the approach experimenting with popular XMC datasets for which GROOV is able to predict meaningful labels outside the given vocabulary while performing on par with state of the art solutions for known labels

2021

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Joint Verification and Reranking for Open Fact Checking Over Tables
Michael Sejr Schlichtkrull | Vladimir Karpukhin | Barlas Oguz | Mike Lewis | Wen-tau Yih | Sebastian Riedel
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)

Structured information is an important knowledge source for automatic verification of factual claims. Nevertheless, the majority of existing research into this task has focused on textual data, and the few recent inquiries into structured data have been for the closed-domain setting where appropriate evidence for each claim is assumed to have already been retrieved. In this paper, we investigate verification over structured data in the open-domain setting, introducing a joint reranking-and-verification model which fuses evidence documents in the verification component. Our open-domain model achieves performance comparable to the closed-domain state-of-the-art on the TabFact dataset, and demonstrates performance gains from the inclusion of multiple tables as well as a significant improvement over a heuristic retrieval baseline.

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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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KILT : a Benchmark for Knowledge Intensive Language TasksKILT: a Benchmark for Knowledge Intensive Language Tasks
Fabio Petroni | Aleksandra Piktus | Angela Fan | Patrick Lewis | Majid Yazdani | Nicola De Cao | James Thorne | Yacine Jernite | Vladimir Karpukhin | Jean Maillard | Vassilis Plachouras | Tim Rocktäschel | Sebastian Riedel
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom knowledge sources, in addition to dedicated infrastructure. To catalyze research on models that condition on specific information in large textual resources, we present a benchmark for knowledge-intensive language tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia, reducing engineering turnaround through the re-use of components, as well as accelerating research into task-agnostic memory architectures. We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline, outperforming more tailor-made approaches for fact checking, open-domain question answering and dialogue, and yielding competitive results on entity linking and slot filling, by generating disambiguated text. KILT data and code are available at https://github.com/facebookresearch/KILT.

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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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Scalable Zero-shot Entity Linking with Dense Entity Retrieval
Ledell Wu | Fabio Petroni | Martin Josifoski | Sebastian Riedel | Luke Zettlemoyer
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off. We present a two-stage zero-shot linking algorithm, where each entity is defined only by a short textual description. The first stage does retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions. Each candidate is then re-ranked with a cross-encoder, that concatenates the mention and entity text. Experiments demonstrate that this approach is state of the art on recent zero-shot benchmarks (6 point absolute gains) and also on more established non-zero-shot evaluations (e.g. TACKBP-2010), despite its relative simplicity (e.g. no explicit entity embeddings or manually engineered mention tables). We also show that bi-encoder linking is very fast with nearest neighbor search (e.g. linking with 5.9 million candidates in 2 milliseconds), and that much of the accuracy gain from the more expensive cross-encoder can be transferred to the bi-encoder via knowledge distillation. Our code and models are available at https://github.com/facebookresearch/BLINK.

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AxCell : Automatic Extraction of Results from Machine Learning PapersAxCell: Automatic Extraction of Results from Machine Learning Papers
Marcin Kardas | Piotr Czapla | Pontus Stenetorp | Sebastian Ruder | Sebastian Riedel | Ross Taylor | Robert Stojnic
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AxCell, an automatic machine learning pipeline for extracting results from papers. AxCell uses several novel components, including a table segmentation subtask, to learn relevant structural knowledge that aids extraction. When compared with existing methods, our approach significantly improves the state of the art for results extraction. We also release a structured, annotated dataset for training models for results extraction, and a dataset for evaluating the performance of models on this task. Lastly, we show the viability of our approach enables it to be used for semi-automated results extraction in production, suggesting our improvements make this task practically viable for the first time. Code is available on GitHub.

2019

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Language Models as Knowledge Bases?
Fabio Petroni | Tim Rocktäschel | Sebastian Riedel | Patrick Lewis | Anton Bakhtin | Yuxiang Wu | Alexander Miller
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as fill-in-the-blank cloze statements. Language models have many advantages over structured knowledge bases : they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train. We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models. We find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (iii) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches. The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems. The code to reproduce our analysis is available at https://github.com/facebookresearch/LAMA.

2018

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Interpretation of Natural Language Rules in Conversational Machine Reading
Marzieh Saeidi | Max Bartolo | Patrick Lewis | Sameer Singh | Tim Rocktäschel | Mike Sheldon | Guillaume Bouchard | Sebastian Riedel
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader’s background knowledge. One example is the task of interpreting regulations to answer Can I...? or Do I have to...? questions such as I am working in Canada. Do I have to carry on paying UK National Insurance? after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as How long have you been working abroad? when the answer can not be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 37k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.

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Wronging a Right : Generating Better Errors to Improve Grammatical Error Detection
Sudhanshu Kasewa | Pontus Stenetorp | Sebastian Riedel
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Grammatical error correction, like other machine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to produce. While writing a program to automatically generate realistic grammatical errors would be difficult, one could learn the distribution of naturally-occurring errors and attempt to introduce them into other datasets. Initial work on inducing errors in this way using statistical machine translation has shown promise ; we investigate cheaply constructing synthetic samples, given a small corpus of human-annotated data, using an off-the-rack attentive sequence-to-sequence model and a straight-forward post-processing procedure. Our approach yields error-filled artificial data that helps a vanilla bi-directional LSTM to outperform the previous state of the art at grammatical error detection, and a previously introduced model to gain further improvements of over 5 % F0.5 score. When attempting to determine if a given sentence is synthetic, a human annotator at best achieves 39.39 F1 score, indicating that our model generates mostly human-like instances.

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Extrapolation in NLPNLP
Jeff Mitchell | Pontus Stenetorp | Pasquale Minervini | Sebastian Riedel
Proceedings of the Workshop on Generalization in the Age of Deep Learning

We argue that extrapolation to unseen data will often be easier for models that capture global structures, rather than just maximise their local fit to the training data. We show that this is true for two popular models : the Decomposable Attention Model and word2vec.

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UCL Machine Reading Group : Four Factor Framework For Fact Finding (HexaF)UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)
Takuma Yoneda | Jeff Mitchell | Johannes Welbl | Pontus Stenetorp | Sebastian Riedel
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

In this paper we describe our 2nd place FEVER shared-task system that achieved a FEVER score of 62.52 % on the provisional test set (without additional human evaluation), and 65.41 % on the development set. Our system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation. Retrieval is performed leveraging task-specific features, and then a natural language inference model takes each of the retrieved sentences paired with the claimed fact. The resulting predictions are aggregated across retrieved sentences with a Multi-Layer Perceptron, and re-ranked corresponding to the final prediction.

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Behavior Analysis of NLI Models : Uncovering the Influence of Three Factors on RobustnessNLI Models: Uncovering the Influence of Three Factors on Robustness
Ivan Sanchez | Jeff Mitchell | Sebastian Riedel
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Natural Language Inference is a challenging task that has received substantial attention, and state-of-the-art models now achieve impressive test set performance in the form of accuracy scores. Here, we go beyond this single evaluation metric to examine robustness to semantically-valid alterations to the input data. We identify three factors-insensitivity, polarity and unseen pairs-and compare their impact on three SNLI models under a variety of conditions. Our results demonstrate a number of strengths and weaknesses in the models’ ability to generalise to new in-domain instances. In particular, while strong performance is possible on unseen hypernyms, unseen antonyms are more challenging for all the models. More generally, the models suffer from an insensitivity to certain small but semantically significant alterations, and are also often influenced by simple statistical correlations between words and training labels. Overall, we show that evaluations of NLI models can benefit from studying the influence of factors intrinsic to the models or found in the dataset used.

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Zero-Shot Transfer Learning for Event Extraction
Lifu Huang | Heng Ji | Kyunghyun Cho | Ido Dagan | Sebastian Riedel | Clare Voss
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Most previous supervised event extraction methods have relied on features derived from manual annotations, and thus can not be applied to new event types without extra annotation effort. We take a fresh look at event extraction and model it as a generic grounding problem : mapping each event mention to a specific type in a target event ontology. We design a transferable architecture of structural and compositional neural networks to jointly represent and map event mentions and types into a shared semantic space. Based on this new framework, we can select, for each event mention, the event type which is semantically closest in this space as its type. By leveraging manual annotations available for a small set of existing event types, our framework can be applied to new unseen event types without additional manual annotations. When tested on 23 unseen event types, our zero-shot framework, without manual annotations, achieved performance comparable to a supervised model trained from 3,000 sentences annotated with 500 event mentions.

2017

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A Supervised Approach to Extractive Summarisation of Scientific Papers
Ed Collins | Isabelle Augenstein | Sebastian Riedel
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.

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SemEval 2017 Task 10 : ScienceIE-Extracting Keyphrases and Relations from Scientific PublicationsSemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
Isabelle Augenstein | Mrinal Das | Sebastian Riedel | Lakshmi Vikraman | Andrew McCallum
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

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Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Martha Palmer | Rebecca Hwa | Sebastian Riedel
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Neural Architectures for Fine-grained Entity Type Classification
Sonse Shimaoka | Pontus Stenetorp | Kentaro Inui | Sebastian Riedel
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In this work, we investigate several neural network architectures for fine-grained entity type classification and make three key contributions. Despite being a natural comparison and addition, previous work on attentive neural architectures have not considered hand-crafted features and we combine these with learnt features and establish that they complement each other. Additionally, through quantitative analysis we establish that the attention mechanism learns to attend over syntactic heads and the phrase containing the mention, both of which are known to be strong hand-crafted features for our task. We introduce parameter sharing between labels through a hierarchical encoding method, that in low-dimensional projections show clear clusters for each type hierarchy. Lastly, despite using the same evaluation dataset, the literature frequently compare models trained using different data. We demonstrate that the choice of training data has a drastic impact on performance, which decreases by as much as 9.85 % loose micro F1 score for a previously proposed method. Despite this discrepancy, our best model achieves state-of-the-art results with 75.36 % loose micro F1 score on the well-established Figer (GOLD) dataset and we report the best results for models trained using publicly available data for the OntoNotes dataset with 64.93 % loose micro F1 score.

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How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis
Ivan Sanchez | Sebastian Riedel
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

One key property of word embeddings currently under study is their capacity to encode hypernymy. Previous works have used supervised models to recover hypernymy structures from embeddings. However, the overall results do not clearly show how well we can recover such structures. We conduct the first dataset-centric analysis that shows how only the Baroni dataset provides consistent results. We empirically show that a possible reason for its good performance is its alignment to dimensions specific of hypernymy : generality and similarity

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Imitation learning for structured prediction in natural language processing
Andreas Vlachos | Gerasimos Lampouras | Sebastian Riedel
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts

Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e.g. autonomous flight from pilot demonstrations. Recently, algorithms for structured prediction were proposed under this paradigm and have been applied successfully to a number of tasks including syntactic dependency parsing, information extraction, coreference resolution, dynamic feature selection, semantic parsing and natural language generation. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Our aim in this tutorial is to have a unified presentation of the various imitation algorithms for structure prediction, and show how they can be applied to a variety of NLP tasks. All material associated with the tutorial will be made available through https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017/.