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Found 16 papers, 4 papers with code
A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs
Negation is one of the most fundamental concepts in human cognition and language, and several natural language inference (NLI) probes have been designed to investigate pretrained language models’ ability to detect and reason with negation.
Multilingual Negation Scope Resolution for Clinical Text
Negation scope resolution is key to high-quality information extraction from clinical texts, but so far, efforts to make encoders used for information extraction negation-aware have been limited to English.
ADaPT: As-Needed Decomposition and Planning with Language Models
Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment.
Putting Humans in the Image Captioning Loop
Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited.
Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memory
Interactive machine learning (IML) is a beneficial learning paradigm in cases of limited data availability, as human feedback is incrementally integrated into the training process.
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop
This paper presents our project proposal for extracting biomedical information from German clinical narratives with limited amounts of annotations.
A survey on improving NLP models with human explanations
Training a model with access to human explanations can improve data efficiency and model performance on in- and out-of-domain data.
Interactive Machine Learning for Image Captioning
We propose an approach for interactive learning for an image captioning model.
MDAPT: Multilingual Domain Adaptive Pretraining in a Single Model
Domain adaptive pretraining, i. e. the continued unsupervised pretraining of a language model on domain-specific text, improves the modelling of text for downstream tasks within the domain.
Comparing Unsupervised Word Translation Methods Step by Step
Cross-lingual word vector space alignment is the task of mapping the vocabularies of two languages into a shared semantic space, which can be used for dictionary induction, unsupervised machine translation, and transfer learning.
Mapping (Dis-)Information Flow about the MH17 Plane Crash
In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash.
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Induction
We study the composition and quality of the test sets for five diverse languages from this dataset, with concerning findings: (1) a quarter of the data consists of proper nouns, which can be hardly indicative of BDI performance, and (2) there are pervasive gaps in the gold-standard targets.
Issue Framing in Online Discussion Fora
In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic.
Why is unsupervised alignment of English embeddings from different algorithms so hard?
This paper presents a challenge to the community: Generative adversarial networks (GANs) can perfectly align independent English word embeddings induced using the same algorithm, based on distributional information alone; but fails to do so, for two different embeddings algorithms.
Limitations of Cross-Lingual Learning from Image Search
Cross-lingual representation learning is an important step in making NLP scale to all the world's languages.
