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07/30/2023
Distractor generation for multiple-choice questions with predictive prompting and large language models
Large Language Models (LLMs) such as ChatGPT have demonstrated remarkabl...
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07/20/2023
Extreme Multi-Label Skill Extraction Training using Large Language Models
Online job ads serve as a valuable source of information for skill requi...
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06/02/2023
Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning
Since performing exercises (including, e.g., practice tests) forms a cru...
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06/01/2023
Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of Biomedical Definitions Generated from Ontological Knowledge
Background: More than 400,000 biomedical concepts and some of their rela...
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05/31/2023
IDAS: Intent Discovery with Abstractive Summarization
Intent discovery is the task of inferring latent intents from a set of u...
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05/22/2023
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance
Timely and accurate extraction of Adverse Drug Events (ADE) from biomedi...
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05/11/2023
Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning
This paper shines a light on the potential of definition-based semantic ...
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02/05/2023
TempEL: Linking Dynamically Evolving and Newly Emerging Entities
In our continuously evolving world, entities change over time and new, p...
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11/15/2022
Neural Bayesian Network Understudy
Bayesian Networks may be appealing for clinical decision-making due to t...
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10/25/2022
Learning to Reuse Distractors to support Multiple Choice Question Generation in Education
Multiple choice questions (MCQs) are widely used in digital learning sys...
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10/21/2022
BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)
This work introduces BioLORD, a new pre-training strategy for producing ...
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10/21/2022
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals
For text classification tasks, finetuned language models perform remarka...
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10/12/2022
EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain
We introduce a high-quality dataset that contains 3,397 samples comprisi...
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09/13/2022
Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction
Skills play a central role in the job market and many human resources (H...
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08/24/2022
Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines
Models for bankruptcy prediction are useful in several real-world scenar...
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06/17/2022
CookDial: A dataset for task-oriented dialogs grounded in procedural documents
This work presents a new dialog dataset, CookDial, that facilitates rese...
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09/20/2021
JobBERT: Understanding Job Titles through Skills
Job titles form a cornerstone of today's human resources (HR) processes....
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08/30/2021
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution
We consider the task of document-level entity linking (EL), where it is ...
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07/05/2021
Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution
We consider a joint information extraction (IE) model, solving named ent...
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04/16/2021
A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks
In online domain-specific customer service applications, many companies ...
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04/08/2021
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders
Powerful sentence encoders trained for multiple languages are on the ris...
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09/26/2020
DWIE: an entity-centric dataset for multi-task document-level information extraction
This paper presents DWIE, the 'Deutsche Welle corpus for Information Ext...
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09/11/2020
Solving Math Word Problems by Scoring Equations with Recursive Neural Networks
Solving math word problems is a cornerstone task in assessing language u...
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01/15/2020
Overly Optimistic Prediction Results on Imbalanced Data: Flaws and Benefits of Applying Over-sampling
Information extracted from electrohysterography recordings could potenti...
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01/14/2020
Block-wise Dynamic Sparseness
Neural networks have achieved state of the art performance across a wide...
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11/21/2019
System Identification with Time-Aware Neural Sequence Models
Established recurrent neural networks are well-suited to solve a wide va...
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03/13/2019
Sub-event detection from Twitter streams as a sequence labeling problem
This paper introduces improved methods for sub-event detection in social...
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08/28/2018
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
Character-level features are currently used in different neural network-...
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08/27/2018
Predefined Sparseness in Recurrent Sequence Models
Inducing sparseness while training neural networks has been shown to yie...
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08/21/2018
Adversarial training for multi-context joint entity and relation extraction
Adversarial training (AT) is a regularization method that can be used to...
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06/25/2018
Prior Attention for Style-aware Sequence-to-Sequence Models
We extend sequence-to-sequence models with the possibility to control th...
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06/20/2018
Jack the Reader - A Machine Reading Framework
Many Machine Reading and Natural Language Understanding tasks require re...
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05/28/2018
DeepProbLog: Neural Probabilistic Logic Programming
We introduce DeepProbLog, a probabilistic logic programming language tha...
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04/20/2018
Joint entity recognition and relation extraction as a multi-head selection problem
State-of-the-art models for joint entity recognition and relation extrac...
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01/02/2018
Character-level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes
Recurrent neural networks are nowadays successfully used in an abundance...
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09/27/2017
An attentive neural architecture for joint segmentation and parsing and its application to real estate ads
In this paper we develop a relatively simple and effective neural joint ...
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08/22/2017
Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales
The amount of content on online music streaming platforms is immense, an...
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08/11/2017
Break it Down for Me: A Study in Automated Lyric Annotation
Comprehending lyrics, as found in songs and poems, can pose a challenge ...
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07/24/2017
Adversarial Sets for Regularising Neural Link Predictors
In adversarial training, a set of models learn together by pursuing comp...
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07/02/2016
Representation learning for very short texts using weighted word embedding aggregation
Short text messages such as tweets are very noisy and sparse in their us...
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06/27/2016
Lifted Rule Injection for Relation Embeddings
Methods based on representation learning currently hold the state-of-the...
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05/09/2016
Efficiency Evaluation of Character-level RNN Training Schedules
We present four training and prediction schedules from the same characte...
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12/02/2015
Learning Semantic Similarity for Very Short Texts
Levering data on social media, such as Twitter and Facebook, requires in...
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11/19/2015