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Found 18 papers, 5 papers with code
Disembodied Machine Learning: On the Illusion of Objectivity in NLP
In this paper, we contextualise this discourse of bias in the ML community against the subjective choices in the development process.
Domain Transfer in Dialogue Systems without Turn-Level Supervision
Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation.
CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs
This work describes the system presented by the CoAStaL Natural Language Processing group at University of Copenhagen.
Sequence Classification with Human Attention
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.
Lexi: A tool for adaptive, personalized text simplification
Most previous research in text simplification has aimed to develop generic solutions, assuming very homogeneous target audiences with consistent intra-group simplification needs.
Multi-task learning for historical text normalization: Size matters
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.
Cross-lingual complex word identification with multitask learning
We approach the 2018 Shared Task on Complex Word Identification by leveraging a cross-lingual multitask learning approach.
Predicting misreadings from gaze in children with reading difficulties
We present the first work on predicting reading mistakes in children with reading difficulties based on eye-tracking data from real-world reading teaching.
Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs
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.
Ranked #8 on
Text Simplification
on PWKP / WikiSmall
(SARI metric)
Learning attention for historical text normalization by learning to pronounce
Automated processing of historical texts often relies on pre-normalization to modern word forms.
Latent Multi-task Architecture Learning
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.
Identifying beneficial task relations for multi-task learning in deep neural networks
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.
KorAP Architecture ― Diving in the Deep Sea of Corpus Data
KorAP is a corpus search and analysis platform, developed at the Institute for the German Language (IDS).
Named Entity Tagging a Very Large Unbalanced Corpus: Training and Evaluating NE Classifiers
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).
