Tim Rocktäschel

Also published as: Tim Rocktaschel


2021

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How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
Prithviraj Ammanabrolu | Jack Urbanek | Margaret Li | Arthur Szlam | Tim Rocktäschel | Jason Weston
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)—a large-scale crowd-sourced fantasy text-game—with a dataset of quests. These contain natural language motivations paired with in-game goals and human demonstrations; completing a quest might require dialogue or actions (or both). We introduce a reinforcement learning system that (1) incorporates large-scale language modeling-based and commonsense reasoning-based pre-training to imbue the agent with relevant priors; and (2) leverages a factorized action space of action commands and dialogue, balancing between the two. We conduct zero-shot evaluations using held-out human expert demonstrations, showing that our agents are able to act consistently and talk naturally with respect to their motivations.

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KILT: 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.

2020

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Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
Joe Stacey | Pasquale Minervini | Haim Dubossarsky | Sebastian Riedel | Tim Rocktäschel
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment classes. These artefacts are exploited by neural networks even when only considering the hypothesis and ignoring the premise, leading to unwanted biases. Belinkov et al. (2019b) proposed tackling this problem via adversarial training, but this can lead to learned sentence representations that still suffer from the same biases. We show that the bias can be reduced in the sentence representations by using an ensemble of adversaries, encouraging the model to jointly decrease the accuracy of these different adversaries while fitting the data. This approach produces more robust NLI models, outperforming previous de-biasing efforts when generalised to 12 other NLI datasets (Belinkov et al., 2019a; Mahabadi et al., 2020). In addition, we find that the optimal number of adversarial classifiers depends on the dimensionality of the sentence representations, with larger sentence representations being more difficult to de-bias while benefiting from using a greater number of adversaries.

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How Decoding Strategies Affect the Verifiability of Generated Text
Luca Massarelli | Fabio Petroni | Aleksandra Piktus | Myle Ott | Tim Rocktäschel | Vassilis Plachouras | Fabrizio Silvestri | Sebastian Riedel
Findings of the Association for Computational Linguistics: EMNLP 2020

Recent progress in pre-trained language models led to systems that are able to generate text of an increasingly high quality. While several works have investigated the fluency and grammatical correctness of such models, it is still unclear to which extent the generated text is consistent with factual world knowledge. Here, we go beyond fluency and also investigate the verifiability of text generated by state-of-the-art pre-trained language models. A generated sentence is verifiable if it can be corroborated or disproved by Wikipedia, and we find that the verifiability of generated text strongly depends on the decoding strategy. In particular, we discover a tradeoff between factuality (i.e., the ability of generating Wikipedia corroborated text) and repetitiveness. While decoding strategies such as top-k and nucleus sampling lead to less repetitive generations, they also produce less verifiable text. Based on these finding, we introduce a simple and effective decoding strategy which, in comparison to previously used decoding strategies, produces less repetitive and more verifiable text.

2019

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NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language
Leon Weber | Pasquale Minervini | Jannes Münchmeyer | Ulf Leser | Tim Rocktäschel
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Rule-based models are attractive for various tasks because they inherently lead to interpretable and explainable decisions and can easily incorporate prior knowledge. However, such systems are difficult to apply to problems involving natural language, due to its large linguistic variability. In contrast, neural models can cope very well with ambiguity by learning distributed representations of words and their composition from data, but lead to models that are difficult to interpret. In this paper, we describe a model combining neural networks with logic programming in a novel manner for solving multi-hop reasoning tasks over natural language. Specifically, we propose to use an Prolog prover which we extend to utilize a similarity function over pretrained sentence encoders. We fine-tune the representations for the similarity function via backpropagation. This leads to a system that can apply rule-based reasoning to natural language, and induce domain-specific natural language rules from training data. We evaluate the proposed system on two different question answering tasks, showing that it outperforms two baselines – BiDAF (Seo et al., 2016a) and FastQA( Weissenborn et al., 2017) on a subset of the WikiHop corpus and achieves competitive results on the MedHop data set (Welbl et al., 2017).

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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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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 cannot 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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Jack the Reader – A Machine Reading Framework
Dirk Weissenborn | Pasquale Minervini | Isabelle Augenstein | Johannes Welbl | Tim Rocktäschel | Matko Bošnjak | Jeff Mitchell | Thomas Demeester | Tim Dettmers | Pontus Stenetorp | Sebastian Riedel
Proceedings of ACL 2018, System Demonstrations

Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions. Providing a set of useful primitives operating in a single framework of related tasks would allow for expressive modelling, and easier model comparison and replication. To that end, we present Jack the Reader (JACK), a framework for Machine Reading that allows for quick model prototyping by component reuse, evaluation of new models on existing datasets as well as integrating new datasets and applying them on a growing set of implemented baseline models. JACK is currently supporting (but not limited to) three tasks: Question Answering, Natural Language Inference, and Link Prediction. It is developed with the aim of increasing research efficiency and code reuse.

2016

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Stance Detection with Bidirectional Conditional Encoding
Isabelle Augenstein | Tim Rocktäschel | Andreas Vlachos | Kalina Bontcheva
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Lifted Rule Injection for Relation Embeddings
Thomas Demeester | Tim Rocktäschel | Sebastian Riedel
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the 5th Workshop on Automated Knowledge Base Construction
Jay Pujara | Tim Rocktaschel | Danqi Chen | Sameer Singh
Proceedings of the 5th Workshop on Automated Knowledge Base Construction

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Learning Knowledge Base Inference with Neural Theorem Provers
Tim Rocktäschel | Sebastian Riedel
Proceedings of the 5th Workshop on Automated Knowledge Base Construction

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Regularizing Relation Representations by First-order Implications
Thomas Demeester | Tim Rocktäschel | Sebastian Riedel
Proceedings of the 5th Workshop on Automated Knowledge Base Construction

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emoji2vec: Learning Emoji Representations from their Description
Ben Eisner | Tim Rocktäschel | Isabelle Augenstein | Matko Bošnjak | Sebastian Riedel
Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media

2015

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Injecting Logical Background Knowledge into Embeddings for Relation Extraction
Tim Rocktäschel | Sameer Singh | Sebastian Riedel
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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WOLFE: An NLP-friendly Declarative Machine Learning Stack
Sameer Singh | Tim Rocktäschel | Luke Hewitt | Jason Naradowsky | Sebastian Riedel
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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Matrix and Tensor Factorization Methods for Natural Language Processing
Guillaume Bouchard | Jason Naradowsky | Sebastian Riedel | Tim Rocktäschel | Andreas Vlachos
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Tutorial Abstracts

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Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction
Sameer Singh | Tim Rocktäschel | Sebastian Riedel
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

2014

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Low-Dimensional Embeddings of Logic
Tim Rocktäschel | Matko Bosnjak | Sameer Singh | Sebastian Riedel
Proceedings of the ACL 2014 Workshop on Semantic Parsing

2013

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WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs
Tim Rocktäschel | Torsten Huber | Michael Weidlich | Ulf Leser
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

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WBI-DDI: Drug-Drug Interaction Extraction using Majority Voting
Philippe Thomas | Mariana Neves | Tim Rocktäschel | Ulf Leser
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)