Papers with Wall Street Journal
Learning Robust and Multilingual Speech Representations (2020.findings-emnlp)
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| Challenge: | Unsupervised speech representation learning has shown success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. |
| Approach: | They evaluate unsupervised speech representation learning representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. |
| Outcome: | The proposed representations improve the recognition performance in 25 phonetically diverse languages and are robust to domain shifts. |
Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples (P18-1)
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| Challenge: | Statistical parsers are often criticized for their performance outside of the domain they were trained on . we show that word representations reduce the need for domain adaptation when the target domain is syntactically similar to the source domain. |
| Approach: | They propose a way to adapt a parser to a syntactically similar target domain using partial annotations. |
| Outcome: | The proposed model increases the accuracy of a parser on the Wall Street Journal by 1.7% over the previous state-of-the-art model. |
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders (D19-1)
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| Challenge: | Existing models that use ground-truth part-of-speech tags are not always available and have significant weaknesses. |
| Approach: | They propose to use deep inside-outside recursive autoencoders to cluster the learned phrase vectors to induce span labels. |
| Outcome: | The proposed model outperforms ELMo and BERT on two versions of the Wall Street Journal dataset and improves over a previous state-of-the-art system that requires additional human annotations by 5 absolute F1 points (19% relative error reduction). |
On the Role of Supervision in Unsupervised Constituency Parsing (2020.emnlp-main)
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| Challenge: | Recent work on unsupervised constituency parsing uses labeled examples for tuning . a few-shot parser with labeles can outperform other approaches by a significant margin . |
| Approach: | They propose to use as few labeled examples as possible for model development . they propose to train existing models on the same labeles they access . |
| Outcome: | The proposed model outperforms other models on the WSJ development set by a significant margin . the proposed model can be further improved by augmentation and self-training . |
Prague Dependency Treebank - Consolidated 1.0 (2020.lrec-1)
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Jan Hajič, Eduard Bejček, Jaroslava Hlavacova, Marie Mikulová, Milan Straka, Jan Štěpánek, Barbora Štěpánková
| Challenge: | Using the standard PDT scheme, the Prague Dependency Treebank-Consolidated 1.0 contains 4 different datasets of Czech, uniformly annotated using the standard scheme. |
| Approach: | They present a richly annotated and genre-diversified language resource, the Prague Dependency Treebank-Consolidated 1.0, which contains 4 different datasets of Czech, uniformly annnotated using the standard PDT scheme. |
| Outcome: | The Prague Dependency Treebank-Consolidated 1.0 contains 4 datasets of Czech, uniformly annotated using the standard PDT scheme. |
AI use in American newspapers is widespread, uneven, and rarely disclosed (2026.acl-long)
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Jenna Russell, Marzena Karpinska, Destiny Akinode, James Zhou, Katherine Thai, Bradley Emi, Max Spero, Mohit Iyyer
| Challenge: | a large-scale dataset of 186K articles from 1.5K newspapers published in the summer of 2025 is audited. |
| Approach: | They audit 186K articles from 1.5K newspapers published in summer of 2025 . they use Pangram, a state-of-the-art AI detector, to detect whether articles are partially or fully AI-generated . |
| Outcome: | The findings highlight the need for greater transparency and updated editorial standards regarding the use of AI in journalism to maintain public trust. |