Papers by Jenna Kanerva
Parse Me if You Can: Artificial Treebanks for Parsing Experiments on Elliptical Constructions (L18-1)
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| Challenge: | ellipsis is a phenomenon present in many natural languages, but it complicates syntactic parsing of the content that is not omitted. |
| Approach: | They analyze outputs of state-of-the-art parsers to learn about parsing accuracy and typical errors from the perspective of elliptical constructions. |
| Outcome: | The proposed treebank is a semi-artificially constructed treebank of ellipsis. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
The FISKMÖ Project: Resources and Tools for Finnish-Swedish Machine Translation and Cross-Linguistic Research (2020.lrec-1)
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Jörg Tiedemann, Tommi Nieminen, Mikko Aulamo, Jenna Kanerva, Akseli Leino, Filip Ginter, Niko Papula
| Challenge: | Finnish and Swedish are the two official languages of Finland. |
| Approach: | They propose to compile a massive corpus of translated material between Finnish and Swedish . they also aim to develop open and freely accessible translation services for those two languages . |
| Outcome: | The project aims to develop open and freely accessible translation services for Finnish and Swedish. |
Out-of-Domain Evaluation of Finnish Dependency Parsing (2022.lrec-1)
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| Challenge: | prevailing practice in academia evaluates model performance on in-domain evaluation data . however, in many real world applications data on which model is applied may differ from training data - a problem that is not addressed by current literature. |
| Approach: | They propose to use Finnish-OOD out-of-domain treebank for out- of-domain evaluation . they propose to include sections more challenging for the general parser . |
| Outcome: | The proposed treebank includes five distinct data sources and a total of 19,382 syntactic words in 2,122 sentences. |
FinGPT: Large Generative Models for a Small Language (2023.emnlp-main)
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Risto Luukkonen, Ville Komulainen, Jouni Luoma, Anni Eskelinen, Jenna Kanerva, Hanna-Mari Kupari, Filip Ginter, Veronika Laippala, Niklas Muennighoff, Aleksandra Piktus, Thomas Wang, Nouamane Tazi, Teven Scao, Thomas Wolf, Osma Suominen, Samuli Sairanen, Mikko Merioksa, Jyrki Heinonen, Aija Vahtola, Samuel Antao, Sampo Pyysalo
| Challenge: | Neural language models excel in many tasks in NLP but are limited to smaller languages. |
| Approach: | They propose two approaches to pretrain large language models for Finnish . they train seven monolingual models from scratch and use Finnish as pretraining data . |
| Outcome: | The proposed model is based on a dataset of Finnish web crawls, news, social media and eBooks. |
Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task (D19-57)
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| Challenge: | Syntactic analysis (parsing) is a fundamental task in natural language processing (NLP). |
| Approach: | They propose to use the Turku neural parser to adapt it to the biomedical domain . they evaluated custom word embeddings, combination with other in-domain resources . |
| Outcome: | The proposed approach achieved a labeled attachment score of 89.7%, the best among task participants. |