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20202026
most citedLearning to Reason for Text Generation from Scientific Tables

11 citations · 34 across the 39 of their papers we have counts for

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Showing 2021 · cs.CLShow all

7 papers · 2 filters

cs.CL2021

Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation

Leonardo F. R. Ribeiro, Jonas Pfeiffer, Yue Zhang +1

Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generate…

cs.CL2021★ 2 cited

Assisting Decision Making in Scholarly Peer Review: A Preference Learning Perspective

Nils Dycke, Edwin Simpson, Ilia Kuznetsov +1

Peer review is the primary means of quality control in academia; as an outcome of a peer review process, program and area chairs make acceptance decisions for each paper based on t…

cs.CL2021★ 11 cited

Learning to Reason for Text Generation from Scientific Tables

Nafise Sadat Moosavi, Andreas Rücklé, Dan Roth +1

In this paper, we introduce SciGen, a new challenge dataset for the task of reasoning-aware data-to-text generation consisting of tables from scientific articles and their correspo…

cs.CL2021

What to Pre-Train on? Efficient Intermediate Task Selection

Clifton Poth, Jonas Pfeiffer, Andreas Rücklé +1

Intermediate task fine-tuning has been shown to culminate in large transfer gains across many NLP tasks. With an abundance of candidate datasets as well as pre-trained language mod…

cs.CL2021

TWEAC: Transformer with Extendable QA Agent Classifiers

Gregor Geigle, Nils Reimers, Andreas Rücklé +1

Question answering systems should help users to access knowledge on a broad range of topics and to answer a wide array of different questions. Most systems fall short of this expec…

cs.CL2021

TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

Kexin Wang, Nils Reimers, Iryna Gurevych

Learning sentence embeddings often requires a large amount of labeled data. However, for most tasks and domains, labeled data is seldom available and creating it is expensive. In t…