Search Results for author:
Found 50 papers, 17 papers with code
A Study on Entity Linking Across Domains”:" Which Data is Best for Fine-Tuning?
In particular, we represent the entities of different KGs in a joint vector space and address the questions of which data is best suited for creating and fine-tuning that space, and whether fine-tuning harms performance on the general domain.
ClusterDataSplit: Exploring Challenging Clustering-Based Data Splits for Model Performance Evaluation
This paper adds to the ongoing discussion in the natural language processing community on how to choose a good development set.
Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization
Evaluations on three model editing benchmarks show that SAUL is a practical and reliable solution for model editing outperforming state-of-the-art methods while maintaining generation quality and reducing computational overhead.
Learning Rules from KGs Guided by Language Models
In this work, our goal is to verify to which extent the exploitation of LMs is helpful for improving the quality of rule learning systems.
Learn it or Leave it: Module Composition and Pruning for Continual Learning
In real-world environments, continual learning is essential for machine learning models, as they need to acquire new knowledge incrementally without forgetting what they have already learned.
FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering
To investigate these aspects, we create and publish a novel TQA evaluation benchmark in English.
Rehearsal-Free Modular and Compositional Continual Learning for Language Models
Continual learning aims at incrementally acquiring new knowledge while not forgetting existing knowledge.
Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings
Attribution scores indicate the importance of different input parts and can, thus, explain model behaviour.
GradSim: Gradient-Based Language Grouping for Effective Multilingual Training
However, not all languages positively influence each other and it is an open research question how to select the most suitable set of languages for multilingual training and avoid negative interference among languages whose characteristics or data distributions are not compatible.
Neighboring Words Affect Human Interpretation of Saliency Explanations
Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models.
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis
In this work, we propose to leverage language-adaptive and task-adaptive pretraining on African texts and study transfer learning with source language selection on top of an African language-centric pretrained language model.
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
Prompting pre-trained language models leads to promising results across natural language processing tasks but is less effective when applied in low-resource domains, due to the domain gap between the pre-training data and the downstream task.
Challenges in Explanation Quality Evaluation
This approach assumes that explanations which reach higher proxy scores will also provide a greater benefit to human users.
Multilingual Normalization of Temporal Expressions with Masked Language Models
The detection and normalization of temporal expressions is an important task and preprocessing step for many applications.
Human Interpretation of Saliency-based Explanation Over Text
In this work, we focus on this question through a study of saliency-based explanations over textual data.
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain
The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task.
Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting
In this paper, we explore possible improvements of transformer models in a low-resource setting.
Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings
For this, we investigate different sources of external knowledge and evaluate the performance of our models on in-domain data as well as on special transfer datasets that are designed to assess fine-grained reasoning capabilities.
Thought Flow Nets: From Single Predictions to Trains of Model Thought
In addition, we conduct a qualitative analysis of thought flow correction patterns and explore how thought flow predictions affect human users within a crowdsourcing study.
Enriched Attention for Robust Relation Extraction
The performance of relation extraction models has increased considerably with the rise of neural networks.
To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning
For this, we study the effects of model transfer on sequence labeling across various domains and tasks and show that our methods based on model similarity and support vector machines are able to predict promising sources, resulting in performance increases of up to 24 F1 points.
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
Deep neural networks and huge language models are becoming omnipresent in natural language applications.
NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction
The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks.
An Analysis of Simple Data Augmentation for Named Entity Recognition
Simple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks.
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering
The user study shows that our models increase the ability of the users to judge the correctness of the system and that scores like F1 are not enough to estimate the usefulness of a model in a practical setting with human users.
NLNDE: The Neither-Language-Nor-Domain-Experts' Way of Spanish Medical Document De-Identification
Natural language processing has huge potential in the medical domain which recently led to a lot of research in this field.
NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection
Named entity recognition has been extensively studied on English news texts.
The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain
With this paper, we publish our annotation guidelines, as well as our SOFC-Exp corpus consisting of 45 open-access scholarly articles annotated by domain experts.
Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text
Although temporal tagging is still dominated by rule-based systems, there have been recent attempts at neural temporal taggers.
Ranked #1 on
Temporal Tagging
on Catalan TimeBank 1.0
On the Choice of Auxiliary Languages for Improved Sequence Tagging
Recent work showed that embeddings from related languages can improve the performance of sequence tagging, even for monolingual models.
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain
Exploiting natural language processing in the clinical domain requires de-identification, i. e., anonymization of personal information in texts.
Type-aware Convolutional Neural Networks for Slot Filling
In particular, we explore different ways of integrating the named entity types of the relation arguments into a neural network for relation classification, including a joint training and a structured prediction approach.
Adversarial Training for Satire Detection: Controlling for Confounding Variables
We therefore propose a novel model for satire detection with an adversarial component to control for the confounding variable of publication source.
CIS at TAC Cold Start 2015: Neural Networks and Coreference Resolution for Slot Filling
Especially, it focuses on the coreference and classification component.
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction
As a result, comparability of models across tasks is missing and their applicability to new tasks is limited.
Adversarial Neural Networks for Cross-lingual Sequence Tagging
We study cross-lingual sequence tagging with little or no labeled data in the target language.
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging
Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words.
Impact of Coreference Resolution on Slot Filling
In this paper, we demonstrate the importance of coreference resolution for natural language processing on the example of the TAC Slot Filling shared task.
Syntactic and Semantic Features For Code-Switching Factored Language Models
The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Corpus-level Fine-grained Entity Typing
This paper addresses the problem of corpus-level entity typing, i. e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist".
Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification
We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction.
Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data
Users often use social media to share their interest in products.
Noise Mitigation for Neural Entity Typing and Relation Extraction
For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction.
Exploring Different Dimensions of Attention for Uncertainty Detection
Neural networks with attention have proven effective for many natural language processing tasks.
Nonsymbolic Text Representation
We introduce the first generic text representation model that is completely nonsymbolic, i. e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text.
Combining Recurrent and Convolutional Neural Networks for Relation Classification
This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks.
Comparing Convolutional Neural Networks to Traditional Models for Slot Filling
We address relation classification in the context of slot filling, the task of finding and evaluating fillers like "Steve Jobs" for the slot X in "X founded Apple".
