Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks
Comments 14 pages, accepted at EMNLP 2022 conference as a full paper
期刊&会议
EMNLP
Conference on Empirical Methods in Natural Language Processing · 会议 · Natural Language Processing
Focus! Relevant and Sufficient Context Selection for News Image Captioning
Comments Findings of EMNLP 2022
Analogical Math Word Problems Solving with Enhanced Problem-Solution Association
Comments Accepted by EMNLP 2022 main conference
Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder
Comments Findings of EMNLP 2022
Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework
Comments EMNLP 2022 - Industry track
Open Relation and Event Type Discovery with Type Abstraction
Comments Accepted to EMNLP 2022. 14 pages
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts
Comments Published as a conference paper at EMNLP 2022 (long). Code available at https://github.com/AkariAsai/ATTEMPT
Large Language Models are Few-Shot Clinical Information Extractors
Comments Accepted as a long paper to The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)
A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering
Comments To be published in the companion proceedings of EMNLP 2022. 17 pages (11 of which are in the appendix), 7 figures (3 of which are in the appendix)
A Major Obstacle for NLP Research: Let's Talk about Time Allocation!
Comments To appear at EMNLP 2022
BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems
Comments Paper accepted by the EMNLP 2022 System Demo Track; We have open-sourced the toolkit at https://github.com/salesforce/botsim
Efficient Adversarial Training with Robust Early-Bird Tickets
Comments EMNLP 2022
Stop Measuring Calibration When Humans Disagree
Comments Accepted at EMNLP 2022
Disentangling Uncertainty in Machine Translation Evaluation
Comments accepted at EMNLP 2022
TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages
Comments EMNLP 2022 Findings. 16 pages, 8 figures, 11 tables. The data and code is publicly available at https://github.com/Genius1237/TyDiP
Abstract Visual Reasoning with Tangram Shapes
Comments EMNLP 2022 long paper
Chaining Simultaneous Thoughts for Numerical Reasoning
Comments Findings of EMNLP 2022
Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora
Comments Published at GEM (https://gem-benchmark.com/workshop) workshop at the Empirical Methods in Natural Language Processing (EMNLP) conference in 2022
Training Language Models with Memory Augmentation
Comments EMNLP 2022. Our code and models are available at https://github.com/princeton-nlp/TRIME
GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models
Comments EMNLP 2022. Code and data are released at https://github.com/WadeYin9712/GeoMLAMA/
AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning
Comments Accepted by EMNLP 2022 findings
Textual Enhanced Contrastive Learning for Solving Math Word Problems
Comments Findings of EMNLP 2022
DiffG-RL: Leveraging Difference between State and Common Sense
Comments Findings of EMNLP 2022. Code available at: https://github.com/ibm/diffg-rl
Towards Generalized Open Information Extraction
Comments EMNLP 2022
Guiding Neural Entity Alignment with Compatibility
Comments EMNLP 2022
Controlled Language Generation for Language Learning Items
Comments 9 pages, 3 figures. Accepted to Industry Track at EMNLP 2022
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition
Comments Findings of EMNLP 2021
Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources
Comments Accepted to Findings of EMNLP 2022. You can view our annotations, contribute to our survey, and view the analysis visualizations on our website at https://multilingual-dataset-survey.github.io
Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality
Comments EMNLP 2022 (16 pages; the first 2 authors contributed equally)
G^3: Geolocation via Guidebook Grounding
Comments Findings of EMNLP 2022
