Search Results for author: Joachim Bingel

Found 18 papers, 5 papers with code

Disembodied Machine Learning: On the Illusion of Objectivity in NLP

no code implementations28 Jan 2021 Zeerak Waseem, Smarika Lulz, Joachim Bingel, Isabelle Augenstein

In this paper, we contextualise this discourse of bias in the ML community against the subjective choices in the development process.

BIG-bench Machine Learning

Domain Transfer in Dialogue Systems without Turn-Level Supervision

1 code implementation16 Sep 2019 Joachim Bingel, Victor Petrén Bach Hansen, Ana Valeria Gonzalez, Paweł Budzianowski, Isabelle Augenstein, Anders Søgaard

Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation.

Dialogue State Tracking Task-Oriented Dialogue Systems

Sequence Classification with Human Attention

1 code implementation CONLL 2018 Maria Barrett, Joachim Bingel, Nora Hollenstein, Marek Rei, Anders S{\o}gaard

Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive bias on many attention functions in NLP.

Abusive Language Classification +4

Lexi: A tool for adaptive, personalized text simplification

no code implementations COLING 2018 Joachim Bingel, Gustavo Paetzold, Anders S{\o}gaard

Most previous research in text simplification has aimed to develop generic solutions, assuming very homogeneous target audiences with consistent intra-group simplification needs.

Lexical Simplification Text Simplification

Multi-task learning for historical text normalization: Size matters

no code implementations WS 2018 Marcel Bollmann, Anders S{\o}gaard, Joachim Bingel

Historical text normalization suffers from small datasets that exhibit high variance, and previous work has shown that multi-task learning can be used to leverage data from related problems in order to obtain more robust models.

Grammatical Error Correction Multi-Task Learning +1

Cross-lingual complex word identification with multitask learning

no code implementations WS 2018 Joachim Bingel, Johannes Bjerva

We approach the 2018 Shared Task on Complex Word Identification by leveraging a cross-lingual multitask learning approach.

Complex Word Identification Lexical Simplification

Predicting misreadings from gaze in children with reading difficulties

no code implementations WS 2018 Joachim Bingel, Maria Barrett, Sigrid Klerke

We present the first work on predicting reading mistakes in children with reading difficulties based on eye-tracking data from real-world reading teaching.

Multi-Task Learning Reading Comprehension +1

Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs

1 code implementation IJCNLP 2017 Fern Alva-Manchego, o, Joachim Bingel, Gustavo Paetzold, Carolina Scarton, Lucia Specia

Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data.

Machine Translation Sentence Compression +1

Latent Multi-task Architecture Learning

2 code implementations23 May 2017 Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, Anders Søgaard

In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses.

Multi-Task Learning

Identifying beneficial task relations for multi-task learning in deep neural networks

1 code implementation EACL 2017 Joachim Bingel, Anders Søgaard

Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data.

Multi-Task Learning

Named Entity Tagging a Very Large Unbalanced Corpus: Training and Evaluating NE Classifiers

no code implementations LREC 2014 Joachim Bingel, Thomas Haider

We describe a systematic and application-oriented approach to training and evaluating named entity recognition and classification (NERC) systems, the purpose of which is to identify an optimal system and to train an optimal model for named entity tagging DeReKo, a very large general-purpose corpus of contemporary German (Kupietz et al., 2010).

Chunking Machine Translation +5

Cannot find the paper you are looking for? You can Submit a new open access paper.