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25 papers · 2 filters
Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits
Julia Kreutzer, David Vilar, Artem Sokolov
Training data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e.g. containing contents from multiple domains or di…
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models
Rakesh Chada, Pradeep Natarajan
The task of learning from only a few examples (called a few-shot setting) is of key importance and relevance to a real-world setting. For question answering (QA), the current state…
Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings
Sawsan Alqahtani, Garima Lalwani, Yi Zhang +2
Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target emb…
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation
David Ifeoluwa Adelani, Miaoran Zhang, Xiaoyu Shen +3
Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g. their gender or ethnicity. Style transfer is an effective…
End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs
Armin Oliya, Amir Saffari, Priyanka Sen +1
Recently, end-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) have delivered promising results using only a weakly supervised dataset. However, thes…
How much pretraining data do language models need to learn syntax?
Laura Pérez-Mayos, Miguel Ballesteros, Leo Wanner
Transformers-based pretrained language models achieve outstanding results in many well-known NLU benchmarks. However, while pretraining methods are very convenient, they are expens…