Papers by Igor Malioutov
Weakly Supervised Headline Dependency Parsing (2022.findings-emnlp)
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| Challenge: | English news headlines have unique syntactic properties documented in linguistics literature since the 1930s. |
| Approach: | They propose to provide the first news headline corpus of annotated syntactic dependency trees to evaluate existing NLP parsers on news headlines. |
| Outcome: | The proposed method improves performance across different news outlets, but is moderated by constructions idiosyncratic to outlet. |
NSTM: Real-Time Query-Driven News Overview Composition at Bloomberg (2020.acl-demos)
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Joshua Bambrick, Minjie Xu, Andy Almonte, Igor Malioutov, Guim Perarnau, Vittorio Selo, Iat Chong Chan
| Challenge: | aggregators consume millions of articles every day, making it difficult to quickly identify key events and miss less-reported stories. |
| Approach: | a new kind of summarization engine was needed to condense large volumes of news into short, easy to absorb points. |
| Outcome: | NSTM can be used to summarize news articles in seconds and quickly and efficiently. |
Semantic Role Labeling as Syntactic Dependency Parsing (2020.emnlp-main)
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| Challenge: | Using propBank-style semantic role labeling, we reduce the task to syntactic dependency parsing. |
| Approach: | They propose to convert SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. |
| Outcome: | The proposed scheme reduces the task of (span-based) PropBank-style semantic role labeling to syntactic dependency parsing. |
Learning Syntax from Naturally-Occurring Bracketings (2021.naacl-main)
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| Challenge: | a new method for learning naturally-occurring bracketings is developed . it uses noisy and incomplete data to induce syntactic structures . |
| Approach: | They propose a partial-brackets-aware structured ramp loss in learning to address this challenge . they show that distantly-supervised models trained on naturally-occurring bracketing data are more accurate . constituency is a foundational building block for phrase-structure grammars, they argue . |
| Outcome: | The proposed model achieves an unlabeled F1 score for constituency parsing on the English WSJ corpus. |
Cross-Register Projection for Headline Part of Speech Tagging (2021.emnlp-main)
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| Challenge: | Part of speech (POS) tagging models are underperforming on headlines due to differences in the register of English news headlines and long-form text. |
| Approach: | They propose to annotate news headlines with POS tags by projecting predicted tags from corresponding sentences in news bodies. |
| Outcome: | The proposed model reduces errors by 23% and 19% on a newly-annotated corpus of over 5,248 English news headlines from the Google sentence compression corpus. |
Diversity-Aware Batch Active Learning for Dependency Parsing (2021.naacl-main)
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| Challenge: | a high annotation cost for dependency parsers is a challenge . batch active learning (AL) is based on batch mode, which is more efficient for annotators to label in bulk. |
| Approach: | They propose to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning. |
| Outcome: | The proposed approach improves on an English newswire corpus by enforcing diversity in the sampled batches. |