Papers by Jennimaria Palomaki
New Protocols and Negative Results for Textual Entailment Data Collection (2020.emnlp-main)
Copied to clipboard
| Challenge: | Natural language inference data has proven useful in benchmarking and as pretraining data for tasks requiring language understanding. |
| Approach: | They propose four alternative protocols to improve annotation quality and diversity . they use 8.5k-example training sets to compare different protocols . |
| Outcome: | The proposed protocols improve the ease of training and quality of the examples. |
Decontextualization: Making Sentences Stand-Alone (2021.tacl-1)
Copied to clipboard
| Challenge: | Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. |
| Approach: | They define a problem of sentence decontextualization by rewriting a sentence to be interpretable out of context while preserving its meaning. |
| Outcome: | The proposed method can be used in question answering and document understanding tasks. |
QED: A Framework and Dataset for Explanations in Question Answering (2021.tacl-1)
Copied to clipboard
Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, Michael Collins
| Challenge: | Existing question answering systems provide no explanation of reasoning that leads to answer . linguistically informed, extensible framework provides explanations in question answering . |
| Approach: | They propose a linguistically informed, extensible framework for explanations in question answering . they propose an expert-annotated dataset of QED explanations built upon a subset of the Natural Questions dataset . |
| Outcome: | The proposed framework improves the ability of untrained raters to spot errors in QA datasets. |
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020.tacl-1)
Copied to clipboard
Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, Jennimaria Palomaki
| Challenge: | Existing models for multilingual modeling are based on a set of typological features that are used to express meaning in languages such as English. |
| Approach: | They present a question-answer-typed question-referenced dataset that covers 11 typologically diverse languages with 204K question-and-answered pairs. |
| Outcome: | The proposed dataset covers 11 typologically diverse languages with 204K question-answer pairs. |