Dirk Weissenborn


2018

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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.

2017

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Making Neural QA as Simple as Possible but not Simpler
Dirk Weissenborn | Georg Wiese | Laura Seiffe
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose a simple heuristic that guides the development of neural baseline systems for the extractive QA task. We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, a composition function that goes beyond simple bag-of-words modeling, such as recurrent neural networks. Our results show that FastQA, a system that meets these two requirements, can achieve very competitive performance compared with existing models. We argue that this surprising finding puts results of previous systems and the complexity of recent QA datasets into perspective.

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Neural Domain Adaptation for Biomedical Question Answering
Georg Wiese | Dirk Weissenborn | Mariana Neves
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Factoid question answering (QA) has recently benefited from the development of deep learning (DL) systems. Neural network models outperform traditional approaches in domains where large datasets exist, such as SQuAD (ca. 100,000 questions) for Wikipedia articles. However, these systems have not yet been applied to QA in more specific domains, such as biomedicine, because datasets are generally too small to train a DL system from scratch. For example, the BioASQ dataset for biomedical QA comprises less then 900 factoid (single answer) and list (multiple answers) QA instances. In this work, we adapt a neural QA system trained on a large open-domain dataset (SQuAD, source) to a biomedical dataset (BioASQ, target) by employing various transfer learning techniques. Our network architecture is based on a state-of-the-art QA system, extended with biomedical word embeddings and a novel mechanism to answer list questions. In contrast to existing biomedical QA systems, our system does not rely on domain-specific ontologies, parsers or entity taggers, which are expensive to create. Despite this fact, our systems achieve state-of-the-art results on factoid questions and competitive results on list questions.

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Neural Question Answering at BioASQ 5B
Georg Wiese | Dirk Weissenborn | Mariana Neves
BioNLP 2017

This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-of-the-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.

2016

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Neural Associative Memory for Dual-Sequence Modeling
Dirk Weissenborn
Proceedings of the 1st Workshop on Representation Learning for NLP

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Event Linking with Sentential Features from Convolutional Neural Networks
Sebastian Krause | Feiyu Xu | Hans Uszkoreit | Dirk Weissenborn
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

2015

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Multi-Objective Optimization for the Joint Disambiguation of Nouns and Named Entities
Dirk Weissenborn | Leonhard Hennig | Feiyu Xu | Hans Uszkoreit
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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DFKI: Multi-objective Optimization for the Joint Disambiguation of Entities and Nouns & Deep Verb Sense Disambiguation
Dirk Weissenborn | Feiyu Xu | Hans Uszkoreit
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)