Papers by Clara Vania
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages (D19-1)
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| Challenge: | Large annotated treebanks are available for only a tiny fraction of the world's languages, and there is a wealth of literature on strategies for parsing with few resources. |
| Approach: | They propose three strategies for improving low-resource parsers: data augmentation, cross-lingual training, and transliteration. |
| Outcome: | The proposed methods improve low-resource parsers by using data augmentation, cross-lingual training, and transliteration. |
WebIE: Faithful and Robust Information Extraction on the Web (2023.acl-long)
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| Challenge: | Existing closed IE datasets are built using Wikipedia, but they have limitations when applied to web domains. |
| Approach: | They propose to annotate 25K triples from WebIE through crowdsourcing and introduce mWebIE, a translation of the annotated set in four other languages. |
| Outcome: | The proposed model trains on 1.6M sentences from the English Common Crawl corpus and includes negative examples to better reflect the data on the web. |
PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants (2025.findings-acl)
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| Challenge: | Existing personalization benchmarks focus on chit-chat, non-conversational tasks, or narrow domains, failing to capture complexities of personalized task-oriented assistance. |
| Approach: | They propose a benchmark to evaluate personalization in task-oriented AI assistants . the benchmark features user profiles equipped with rich preferences and interaction histories . |
| Outcome: | The proposed benchmark features user profiles equipped with rich preferences and interaction histories . it also features a judge agent and user agent that employs the LLM-as-a-Judge paradigm . |
Comparing Test Sets with Item Response Theory (2021.acl-long)
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Clara Vania, Phu Mon Htut, William Huang, Dhara Mungra, Richard Yuanzhe Pang, Jason Phang, Haokun Liu, Kyunghyun Cho, Samuel R. Bowman
| Challenge: | Recent results from large pretrained models show that many datasets are saturated and unlikely to detect further progress. |
| Approach: | They evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. |
| Outcome: | The proposed datasets are saturated and unlikely to detect future improvements. |
Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection (2021.emnlp-tutorials)
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| Challenge: | Developing a theory of crowdsourcing for practical language problems remains an open challenge . |
| Approach: | This tutorial exposes NLP researchers to data collection crowdsourcing methods and principles through case studies. |
| Outcome: | This tutorial exposes NLP researchers to various data collection crowdsourcing methods and practices through case studies. |
Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020.acl-main)
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Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, Samuel R. Bowman
| Challenge: | Unsupervised pretraining has recently pushed the state of the art on many natural language understanding tasks. |
| Approach: | They perform a large-scale survey on a pretrained RoBERTa model with 110 intermediate-target task combinations and 25 probing tasks to reveal the specific skills that drive transfer. |
| Outcome: | The proposed model is trained on 110 intermediate-target task combinations and compared with 25 probing tasks to reveal the specific skills that drive transfer. |
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? (2021.acl-long)
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| Challenge: | Despite the importance of datasets for natural language understanding, there has been little attention on crowdsourcing methods for collecting datasets. |
| Approach: | They compare the effectiveness of crowdsourcing methods for boosting NLU example difficulty with training crowdworkers instead of expert judgments. |
| Outcome: | The proposed method is ineffective for boosting NLU example difficulty, but it is not effective for training crowdworkers and qualifying workers based on expert judgments. |
IndoNLI: A Natural Language Inference Dataset for Indonesian (2021.emnlp-main)
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| Challenge: | XLM-R model outperforms other pre-trained models in annotated data. |
| Approach: | They adapt the data collection protocol for MNLI and collect 18K sentence pairs annotated by crowd workers and experts. |
| Outcome: | The proposed dataset outperforms other pre-trained models on the expert-annotated data. |
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models (2020.emnlp-main)
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| Challenge: | Pretrained language models use cultural biases implicitly, causing harm . identifying and quantifying learnt biase enables us to measure progress . |
| Approach: | They propose a benchmark to measure social bias in pretrained language models . they use 1508 examples that cover stereotypes dealing with nine types of bias . |
| Outcome: | The proposed benchmark focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups. |
Asking Crowdworkers to Write Entailment Examples: The Best of Bad Options (2020.aacl-main)
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| Challenge: | Large-scale natural language inference datasets are available for non-expert crowdsourcing. |
| Approach: | They propose to use crowdworker writing to create candidate pairs for annotation . they find crowdworkers writing is the best known protocol for entailment data . |
| Outcome: | The proposed protocol outperforms the baseline protocol in evaluations of generalization and transfer learning. |
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too (2020.aacl-main)
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Jason Phang, Iacer Calixto, Phu Mon Htut, Yada Pruksachatkun, Haokun Liu, Clara Vania, Katharina Kann, Samuel R. Bowman
| Challenge: | a study of intermediate-task training in monolingual English shows that it improves model performance on non-English language understanding tasks. |
| Approach: | They evaluate whether English intermediate-task training is still helpful on non-English target tasks . BUCC and Tatoeba sentence retrieval tasks see large improvements . |
| Outcome: | The proposed model outperforms existing models on non-English language understanding tasks. |
UniMorph 4.0: Universal Morphology (2022.lrec-1)
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Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Abbott Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud’hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova
| Challenge: | The Universal Morphology project provides broad-coverage instantiated morphological inflection tables for hundreds of diverse languages. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. |
| Outcome: | The proposed schema has added 66 new languages, including 24 endangered languages. |
What do character-level models learn about morphology? The case of dependency parsing (D18-1)
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| Challenge: | morphologically rich languages require character-level input models to learn morphology, but some models are poor at disambiguating some words . authors of this study show that character- level models learn a lot from input input . explicit modeling of morphologies is expensive and expensive, authors say . |
| Approach: | They compare character-level models to an oracle with explicit morphological analysis . they show that explicitly modeling morphology improves their best model . |
| Outcome: | The results show that character-level models learn morphology better than word models . the authors compare character-based models to oracles on 12 languages with morphological typologies . |