Our paper was accepted for EMNLP 2022
[item 1]
■Bibliographic Information.
Machel Reid, Graham Neubig. “Learning to Model Editing Processes”, Conference on Empirical Methods in Natural Language Processing (EMNLP 2022).
Abstract
Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced edit-based models for various tasks (such as neural In this work, we propose modeling editing processes, modeling the whole process of iterative refinement and editing. In this work, we propose modeling editing processes, modeling the whole process of iteratively generating sequences. We form a conceptual framework to describe the likelihood of multi-step edits, and We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multi-step edits. task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits.
[second book].
■Bibliographic Information.
Machel Reid, Victor Zhong, Suchin Gururangan, Luke Zettlemoyer. “M2D2: A Massively Multi-domain Language Modeling Dataset “, Conference on Empirical Methods in Natural Language Processing (EMNLP 2022).
Summary
We present M2D2, a fine-grained, massively multi-domain corpus for studying domain adaptation in language models (LMs). Using ontologies derived from Wikipedia and ArXiv This two-level hierarchy enables the study of relationships between domains and their effects on in- and out-of We also present a number of insights into the nature of effective domain adaptation To improve in-domain performance, we show the benefits of adapting the LM along a domain hierarchy; adapting to smaller amounts of fine-grained domain specific data can lead to larger in-domain performance gains than larger amounts of weakly relevant data. We further demonstrate a trade-off between in-domain specialization and out-of-domain generalization within and across We further demonstrate a trade-off between in-domain specialization and out-of-domain generalization within and across ontologies, as well as a strong correlation between out-of-domain performance and lexical overlap between domains.
