Search Results for author: Carolin Lawrence

Found 22 papers, 7 papers with code

State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions

no code implementations10 Dec 2022 Cheng Wang, Carolin Lawrence, Mathias Niepert

We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications.

Memorization Object Recognition +1

Multi-Source Survival Domain Adaptation

no code implementations1 Dec 2022 Ammar Shaker, Carolin Lawrence

With the rise of machine learning, survival analysis can be modeled as learning a function that maps studied patients to their survival times.

Domain Adaptation Survival Analysis

Human-Centric Research for NLP: Towards a Definition and Guiding Questions

no code implementations10 Jul 2022 Bhushan Kotnis, Kiril Gashteovski, Julia Gastinger, Giuseppe Serra, Francesco Alesiani, Timo Sztyler, Ammar Shaker, Na Gong, Carolin Lawrence, Zhao Xu

With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users.

A Human-Centric Assessment Framework for AI

no code implementations25 May 2022 Sascha Saralajew, Ammar Shaker, Zhao Xu, Kiril Gashteovski, Bhushan Kotnis, Wiem Ben Rim, Jürgen Quittek, Carolin Lawrence

Inspired by the Turing test, we introduce a human-centric assessment framework where a leading domain expert accepts or rejects the solutions of an AI system and another domain expert.

milIE: Modular & Iterative Multilingual Open Information Extraction

no code implementations ACL 2022 Bhushan Kotnis, Kiril Gashteovski, Daniel Oñoro Rubio, Vanesa Rodriguez-Tembras, Ammar Shaker, Makoto Takamoto, Mathias Niepert, Carolin Lawrence

In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction.

Open Information Extraction

AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark

1 code implementation ACL 2022 Niklas Friedrich, Kiril Gashteovski, Mingying Yu, Bhushan Kotnis, Carolin Lawrence, Mathias Niepert, Goran Glavaš

Open Information Extraction (OIE) is the task of extracting facts from sentences in the form of relations and their corresponding arguments in schema-free manner.

Open Information Extraction

VEGN: Variant Effect Prediction with Graph Neural Networks

no code implementations25 Jun 2021 Jun Cheng, Carolin Lawrence, Mathias Niepert

In contrast, we propose VEGN, which models variant effect prediction using a graph neural network (GNN) that operates on a heterogeneous graph with genes and variants.

Explaining Neural Matrix Factorization with Gradient Rollback

1 code implementation12 Oct 2020 Carolin Lawrence, Timo Sztyler, Mathias Niepert

Moreover, we show theoretically that the difference between gradient rollback's influence approximation and the true influence on a model's behavior is smaller than known bounds on the stability of stochastic gradient descent.

Influence Approximation Knowledge Base Completion +1

Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders

no code implementations6 Apr 2020 Bhushan Kotnis, Carolin Lawrence, Mathias Niepert

Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries.

Knowledge Graphs Link Prediction +1

Attending to Future Tokens For Bidirectional Sequence Generation

1 code implementation IJCNLP 2019 Carolin Lawrence, Bhushan Kotnis, Mathias Niepert

Treated as a node in a fully connected graph, a placeholder token can take past and future tokens into consideration when generating the actual output token.

Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss

1 code implementation TACL 2019 Laura Jehl, Carolin Lawrence, Stefan Riezler

We show that bipolar ramp loss objectives outperform other non-bipolar ramp loss objectives and minimum risk training (MRT) on both weakly supervised tasks, as well as on a supervised machine translation task.

Machine Translation Semantic Parsing +1

Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing

1 code implementation29 Nov 2018 Carolin Lawrence, Stefan Riezler

In semantic parsing for question-answering, it is often too expensive to collect gold parses or even gold answers as supervision signals.

Question Answering Semantic Parsing

Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback

1 code implementation ACL 2018 Carolin Lawrence, Stefan Riezler

Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system.

Semantic Parsing

Counterfactual Learning for Machine Translation: Degeneracies and Solutions

no code implementations23 Nov 2017 Carolin Lawrence, Pratik Gajane, Stefan Riezler

Counterfactual learning is a natural scenario to improve web-based machine translation services by offline learning from feedback logged during user interactions.

Machine Translation Translation

Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation

no code implementations EMNLP 2017 Carolin Lawrence, Artem Sokolov, Stefan Riezler

The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that were predicted by another, historic SMT system.

Machine Translation Structured Prediction +1

Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation

no code implementations28 Jul 2017 Carolin Lawrence, Artem Sokolov, Stefan Riezler

The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that were predicted by another, historic SMT system.

Machine Translation Translation

NLmaps: A Natural Language Interface to Query OpenStreetMap

no code implementations COLING 2016 Carolin Lawrence, Stefan Riezler

We present a Natural Language Interface (nlmaps. cl. uni-heidelberg. de) to query OpenStreetMap.

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